VIII Latin American Conference on Biomedical Engineering and XLII National Conference on Biomedical Engineering

Studies on EEG involve great amount of data to be processed and analyzed, requiring valuable time that the researchers could spend on more important tasks. On this work we developed a software that incorporates pre-processing algorithms like visualization and windowing tools, band pass filter and artifact removal tools, along with the machine learning algorithms: K-Means to group the data and Decision Trees to classify it. We expect that EEG-PML facilitates researchers work and, with the help of the machine learning algorithms, the studies over the EEG data can advance over new areas of research.

For the reduction of noise and extraction of the evoked potentials several methods have been reported in the literature based on the average of signals, which try to counteract the limitations of the classical technique used for the signal enhancement, which is the coherent average. The quality of the resulting signal can be evaluated using measurements in the time domain or the frequency domain. The objective of this work is to evaluate the performance of different average methods using quality measures in the time domain to determine which combination of an average method with quality measurement is better adjusted to the detection of transient auditory evoked potentials. To fulfill this objective, the classic coherent average, the median, the weighted average and four other versions of the robust average based on the median are tested. The quality measures used to compare the different average methods are the correlation coefficient, the standard deviation ratio and the variance of the single point. It was obtained as a result that the robust method Tanh mean, together with the measure of the variance of single point offers the best results.

Maternal cardio-electrohysterographic coupling (MCEC), which is defined as the interaction of uterine electrical activity and maternal heart rate, is a physiological phenomenon poorly explored in the obstetric field. In this work, we evaluated the mechanism of MCEC before and during labor based on the Bivariate Phase-Rectified Signal Averaging Analysis (BPRSA).A group of women in the third trimester of pregnancy (TT = 44) and another group during active labor (AL = 51) were studied at the Maternal and Childbirth Research Centre (CIMIGen, Mexico City). We extracted 20 min of maternal heart-rate time series as well as the envelopes of the uterine electrical activity signals (electrohysterogram) obtained from a high-frequency recorded electrocardiogram. The coupling between uterine electrical activity and maternal heart rate was analyzed by BPRSA. Significant differences were found between TT vs. AL for cross-correlation of BPRSA signals (0.93 vs. 0.97, p < 0.01), respectively. These results suggest that during active labor, a higher MCEC is observed in comparison to the third trimester of pregnancy. The presence of increased MCEC could be involved in the physiological manifestation of labor.

The objective of this work is to automatically identify basic hand movements: Opening, Closing, Bending, Extension, Pronation and Supination, including the Resting condition. Feature extraction was implemented making use of three approaches: time, frequency and time-frequency domains, obtaining the characteristics Mean Absolute Value (MAV), Root Mean Square (RMS), Wave Length (WL), Autoregressive Coefficients (AR) and Discrete Wavelet Transform (DWT). Principal Component Analysis (PCA) was applied for dimensionality reduction and classification was performed using Linear Discriminant Analysis (LDA). As a result it was possible to identify the movements with success rates that reached 92% with the hybrid vectors conformed by the coefficients MAV, RMS and AR.

Birth weight (BW) is an important indicator of neonatal well–being associated with multiple adverse conditions and thus its early estimation may be crucial for timely treatment. In this paper we propose a BW estimation strategy based on Support Vector Regression of a set of multimodal maternal–fetal features obtained in pregnancy’s first trimester. The obtained results show an average difference of 250 g between the estimated and the real BW, with percentage errors below 3% in all cases. These results contrast with other reported studies, that estimate BW very close to delivery.

Osteoporosis is a systemic disease characterized by low bone mass that enhance bone fragility and increases fracture risk. Just in 2005, a total of 21,000 hip fractures were registered in Mexico and it is expected to rise within the next thirty years. In order to reduce fractures, physicians at INR-LGII are implementing exercise programs focused on the improvement of muscle strength to have a better bone quality. This program has electromyography and strength as output variables which need to be processed for interpretation; this signal processing is performed by an algorithm that delivers the voluntary maximum isometric strength, as well as mean and length in seconds of the electromyography envelope.

The use of artificial neural networks (ANNs) has been very helpful in carrying out prediction, classification and data optimization tasks. In this work, ANNs were used to predict the diagnosis for breast cancer in a women population with overweight or obese and possible diabetes, coupled with a pre or postmenopausal stage and compared against other machine learning techniques reported in literature. The algorithms used to train the ANNs models were Scaled Conjugate Gradient, Resilient Backpropagation and Conjugate Gradient Backpropagation with Powell Beale Restarts. The algorithms results were compared with the original dataset creator’s work, as well as other authors using the same dataset for classification task, a better classification was accomplished using this work ANNs. With four predictors the next values were obtained, AUC = 0.96, sensitivity = 0.96, specificity = 0.96 and Youden index = 0.92. With nine predictors the next values were obtained, AUC = 0.96, sensitivity = 0.95, specificity = 0.97 and Youden index = 0.92. Different strategies are suggested to improve the results, exploring more hidden layers and a different validation method.

The acoustic measurement of the severity of the symptoms present in pathological voice is an active research area, for being inexpensive and non invasive. Periodicity perturbations are among the most frequently used, requiring the previous extraction of the individual glottal pulse boundaries. In this paper we explore the performances of methods for detecting glottal pulse boundaries as implemented by freely available software (Praat, intended for phonetic studies) vs. a research-grade pulse cycle detector (reported as a super-resolution method). We compare the sequences of pulse markers as obtained by two of Praat’s internal implementations and the super-resolution method against the hand-marked reference sequence in a dataset of pathological sustained vowels from a well-known database. A group of performance measures is extracted from this comparison, using a Dynamic-Time Warping alignment procedure. The measures obtained show the pros and cons of each alternative. Researchers and clinicians must be aware of the benefits of selecting either approach.

Heart rate (HR) and respiratory rate (RR) vital signs provide useful cardiorespiratory information, and their monitoring is routinely carried out in clinical settings via electrocardiography, photoplethysmography, and capnography. However, these and other specialized biomedical devices are not easily translated to everyday use outside clinical and research settings. Hence, there is still a need for HR and RR monitoring devices that could be used on a daily basis by the general population. In this study, we employed a contact approach to estimate both HR and RR directly from image plethysmography (iPPG) signals extracted from smartphone-acquired videos. Video recordings of the fingertips from eight (N = 8) volunteers were acquired with an Android smartphone while the subjects performed metronome breathing maneuvers. The iPPG waveform was extracted via the Extended Blind End-member and Abundance Extraction (EBEAE) algorithm. Simultaneous ECG recordings were used to compute reference HR and RR time series. We found that iPPG-based estimates are highly correlated by those from ECG-derived ones (HR: $$ \rho = 0.9953 $$ , RR: $$ \rho = 0.9733 $$ with low normalized root-mean-square errors (HR: $$ NRMSE = $$ 0.0855, RR: $$ NRMSE = 0.3074 $$ for both HR and RR estimation. The obtained results corroborate the feasibility of using contact iPPG methods to accurately estimate not only HR, but also RR without specialized biomedical devices.

In this work, three different classification methods (multi-layer perceptron, support vector machine and decision tree) were used to automatically discern between six wrist movements (palmar flexion, palmar extension, radial deviation, ulnar deviation, supination and pronation of the hand) via time-domain and time-frequency features extracted from electromyographic signals (EMG) of the forearm muscles acquired in a multichannel fashion (eight channels). EMG signals of thirty ( $$N=30$$ ) healthy volunteers were acquired while they were performing consecutive repetitions of the six different wrist movements. Data processing included filtering, signal segmentation, feature extraction and classification using the above-mentioned methods. Finally, the results obtained with both time-domain and time-frequency features were compared. In the tests carried out with time-domain features up to 98% of correct classifications were obtained and up to 95% were obtained with the time-frequency features. These results look promising and we are currently working on their implementation in a robotic wrist rehabilitation system.

The perception of time is a fundamental part of our daily life. Several studies have reported that time estimation involves the communication and synchronization of neural assemblies that decode the duration of time. Therefore, the purpose of the present study is to evaluate the interaction between different brain areas during time estimation. Brain electrical activity was recorded during a time perception task and correlation matrices were calculated using different frequency bands. Graph networks show an important contribution of fronto-central regions and left hemisphere. The estimation of time could involve the recruitment of attentional resources and the inhibition of motor responses to make a temporal prediction, by this way possibly monitoring the “flow of time”.

Parkinson’s disease (PD) has a high impact on motor and cognitive impairment. Recent studies have suggested that abnormal beta band activity (13–35 Hz) in local field potentials (LFP) within the Subthalamic Nucleus (STN) are associated with PD’s symptoms. Moreover, these oscillations are characterized by non-stationary behavior throughout the LFP defined as beta bursts. Despite of the currently widespread framework for evaluating beta bursts based on Continuous Wavelet Transform (CWT) coefficients, a carefully methodological investigation is required, especially when dealing with shorter intraoperative recordings in the context of deep brain stimulation (DBS). Aiming to investigate methodological dependence in beta burst detection, this work presents a comparison between wavelets analysis vs. Hilbert Transform (HT) magnitude for beta bursts duration evaluation considering different percentiles thresholds. Signals from 17 hemispheres of 12 tremor dominant PD patients undergoing stereotaxic surgery under rest and movement conditions were investigated. Bursts durations evaluated by both methods were capable of detecting significant differences between rest vs. movement conditions, depending on the adopted percentile threshold. These results point to a better comprehension of beta burst activity under daily motor tasks as also reveals a methodological dependence on burst characterization, being the HT method more prone to detect beta bursts with shorter durations in contrast to CWT.

Despite the different nature of sounds and electrophysiological potentials, there are certain similarities between these two kinds of signals. Therefore, feature extraction methods used in both cases are based on similar signal processing concepts. In this work, we used a set of audio features based on time, and frequency domains, including Mel-Frequency Cepstral Coefficients (MFCC), which were applied to electroencephalographic signals (EEG) recorded for Motor Imagery (MI) tasks. A classification stage using Multilayer Perceptron (MLP) was implemented, to evaluate the accuracy to differentiate two task of Motor Imagery, for each proposed descriptor, as well as for groups of descriptors. Moreover, the influence of the length size windows was studied with this approach. The results suggest that MFCC are more robust than other descriptors through different window lengths, achieving high classification accuracy both individually and in the group of MFFC filters.

Cortical Auditory Evoked Potential (CAEP) has been used to understand the base of physiological processes of speech perception; in order to establish objectively speech-feature discrimination abilities in a subject. The aim of this work was to localize the brain areas of CAEPs activated when four disyllabic Spanish words were used on a passive listening paradigm in normal hearing adults. The clustering of Equivalent Current Dipoles, based on Independent Component Analysis, was used to localize brain areas activated with four words; which were selected from a list of words with a linguistic frequency approach. Our results show a clear division between words: (1) two with an anterior and posterior cingulate location both, and (2) two located at the Wernicke’s area; those areas are involved on speech comprehension. This study could contribute in understanding of the neuronal substrates involved in the perception of words in Spanish and it could be used to measure objectively the performance of subject native speakers of Spanish, users of Hearing Aids and/or Cochlear Implants.

This article presents a human heart health status classification based on a public database that contains hospital recordings of various heart sounds using a digital stethoscope. Three different sounds were analyzed in this study; normal heart sounds, heart murmur sounds and extra systolic sounds. Ten general and statistical features and 16 parameters of the classical techniques; Linear Predictive Coding (LPC) and Cepstral Frequency-Mel Coefficients (MFCC) were calculated to create a numerical database. Through the genetic algorithm Galgo, the most significant features of all cardiac audio samples were extracted. These features were analyzed with three different classification methods; Logistic Regression (LN), Neural Networks (NN) and Suport Vector Machine (SVM). The area under the curve (ROC) and the accuracy (ACC) were the metrics used to evaluate the classifiers. The Neural Network classifier with the data normalized gave the best results with a ROC = 0.7558. While the same classifier with the data not normalized gave the worst results with a ROC = 0.6571.

In this study, dynamics of the response to an orthostatic challenge was investigated by the combination of an Empirical Mode Decomposition based method and the time varying nonlinear Point Prediction Error algorithm, used for the analysis of heart rate variability (HRV) calculated from time series of beat-to-beat intervals (BBI), and systolic blood pressure (SBP) in patients with vasovagal syncope (VVS) and healthy subjects to provide more information on the mechanisms leading to changes of the autonomic nervous system. This study included 7 young female patients with vasovagal syncope and 7 age-matched female controls. The subjects were enrolled in a head-up tilt (HUT) test, breathing normally, including 5 min of supine position, 15 s of head-up tilting and 18 min of 70 $$^\circ $$ orthostatic phase. This time-variant, frequency-selective nonlinear analysis seemed to be suitable to distinguish between patients and healthy subjects even in the transition phase. The results allowed us to differentiate the response of patients with VVS and healthy subjects, mainly in the HF band of both the BBI and the SBP time series.

Alzheimer disease (AD) is the most common type of dementia and one of the most serious mental health problems. Based on data analysis for classification of dementia, it is possible to distinguish the subjects into three groups: cognitively normal (CN), mild cognitive impairment (MCI) and AD. In this paper, the information of 628 patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf .) database was used which contains 2163 features in order to select the most significant features that allows to the classification of CN vs. MCI/AD, the genetic package GALGO was applied to the features in which four features were selected and four classification techniques were used: Logistic Regression (LR), Random Forest (RF), Artificial Neural Networks (ANN) and Support Vector Machines (SVM). A cross validation of 70% training 30% test. According to the results, the technique with the most significant area under the curve (AUC) for the classification the subjects was LR presenting a value of 0.842.

Deep brain stimulation (DBS) of the subthalamic nuclei (STN) is the most used surgical treatment to improve motor skills in patients with Parkinson’s disease (PD) who do not respond well to pharmacological treatment. Currently, DBS operates in open-loop mode stimulating with constant parameters. A way to improve the therapy is modeling a closed-loop DBS system that automatically adjusts the stimulation parameters based on the clinical and/or neural state of the patient. Fuzzy logic was used to design three models: two Mamdani-type models, varying the defuzzification method, and a Sugeno-type one. The inputs for all models were the beta oscillation power calculated from local field potential and the magnitude of the acceleration recorded from a smart watch. Total electrical energy delivered (TEED) and theoretical charge density in the STN (Q) was calculated in order to evaluate the performance of the algorithms. The model with the lowest TEED was the Mamdani-Bisector type with 136.80 ± 59.07 μW, followed by the Mamdani-Centroid type with 138.90 ± 57.22 μW, being 66% lower than open loop DBS. Mamdani-Centroid had the lowest maximum Q and a smooth output for the trials tested, with gradual changes in the control surface. Therefore, the Mamdani-Centroid model resulted to be the best control strategy to implement a closed-loop DBS system in our study.

This paper addresses the cerebral cortex maps construction from EEG signals getting an information simplification method for an emotional state phenomenon description. Bi-dimensional density distribution of main signal features are identified and a comparison to a previous approach is presented. Feature extraction scheme is performed via windowed EEG signals Stationary Wavelet Transform with the Daubechies Family (1–10); nine temporal and spectral descriptors are computed from the decomposed signal. Recursive feature selection method based on training a Random forest classifier using a one-vs-all scheme with the full features space, then a ranking procedure via gini importance, eliminating the bottom features and restarting the entire process over the new subset. Stopping criteria is the maximum accuracy. The main contribution is the analysis of the resulting subset features as a proxy for cerebral cortex maps looking for the cognitive processes understanding from surface signals. Identifying the common location of different emotional states in the central and frontal lobes, allowing to be strong parietal and temporal lobes differentiators for different emotions.

Heart rate variability (HRV) has been used to indirectly assess and study the autonomic nervous system (ANS). For the analysis of HRV, two different time windows are recommended.For certain physiological conditions, neither of the standard time windows are appropriate for evaluating the very low frequency (VLF) band of HRV. In this work, we evaluate HRV using two different time windows, one of 5 min and the other of 50 min. Analyses were conducted in both the time and frequency domains, with an emphasis on VLF content. Ten patients with chronic kidney disease participated in this study. The electrocardiogram signal was measured during their hemodialysis therapy. Time domain parameters such as the mean heart rate, mean R-R intervals, standard deviation of normal-to-normal intervals (SDNN), and root mean square of standard deviations (RMSSD) are reported, along with frequency domain parameters such as mean power of the very low frequency (VLF), low frequency (LF) and high frequency (HF) bands, as well as the LF/HF ratio.Significant differences were found in the SDNN for 5 min as compared to 50 min (41.13 ± 5.40 ms, 52.85 ± 2.63 ms, p < 0.000), and the VLF power spectra for 5 and 50 min were 1271.54 ± 406.52 ms2 and 1561.99 ± 170.62 ms2 (p = 0.02), respectively.

According to the World Health Organization, 50 million people have epilepsy with 80% of them living in low- and middle-income countries. Three quarters of these do not receive the treatment they need due to delays in interpreting electroencephalograms (EEGs). This paper presents a Machine learning model to support the diagnosis of pediatric epilepsy in semi-automatic way. The model was built from more than 100 pediatric EEGs, with a diagnosis of epileptic seizure. The results achieved using the software were compared with annotations made by a pediatric neurologist, reaching up to 85% agreement. In addition, the neurologists stated that, during the evaluation of a 30-min EEG, the system allowed them to save up to half of the time that usually takes. The tool herein presented facilitates the study and evaluation of pediatric EEGs using a semi-automatic classification of EEG signals and it can be used in the diagnosis of pediatric epilepsy.

The virtual reality (VR) technology has been able to emulate physical places with realistic details, useful for multiple areas such as rehabilitation, education, entertainment, etc. This is possible because virtual environments can emulate the sense of “presence” that the user undergoes in the real world. The present study aimed to compare the typical videogame “Power Solitaire” in both scenarios, real and virtual one, by monitoring presence by estimating the alpha power spectral density on 14 electroencephalographic channels. As well, it was used three questionnaires (“Immersive Tendencies”, “Presence”, and “Slater Usoh-Steed”) to check the level of Presence. For this study, seven volunteers were recruited, and an Occulus Rift Headset and a headset “Emotiv epoc” were used. The study showed that “Power Solitaire” videogame rendered in VR mode induced the similar sense of presence experienced in the real world in accordance with (1) a good movement and auditory response of VR, (2) high quality of user-system interaction, and (3) similar level of alpha neural synchrony over the frontal lobe, which reflects sensory decoding, level of attention, and readiness to perform a task.

This paper presents a study of the heart rate variability (HRV) and respiratory rate variability (RRV) during daytime and nighttime sleep, considering the sleep stages. Eleven healthy female shift workers of 20–54 years old, were recorded during the sleep period. Detrended fluctuation analysis (DFA) was used to assess the short and long-range correlations of time series. Furthermore, the bivariate signal (HRV - RRV) was evaluated by means of the multivariate DFA. The results showed significant differences between the sleep stages. Nevertheless, the mean values of the HRV and RRV exponents did not show meaningful variability between daytime and nighttime sleep. The results suggest that there are changes in the central nervous system, in the regulation of heart and respiratory rate during the sleep stages. Also, the correlations of nighttime sleep are preserved during daytime sleep, which could be due to the adaptability of sleep to changes due to shifting work, keeping correlation properties of HRV and RRV.

Myocardial infarction is a leading cause of death worldwide. A 12-lead electrocardiogram (ECG) recording is commonly performed to diagnose this pathology. In this paper, we explored temporal and morphological features extracted from multi-lead ECG signals to classify subjects from the PTB Diagnostic ECG database into healthy control and myocardial infarction using a support vector machine binary classifier. After delineating the 12-lead ECG signals with a wavelet transform-based method, a unique set of characteristic points was obtained for the ECG leads by suppressing outliers and by taking the average of the remaining points. Then, mathematical operations (average, standard deviation, skewness, etc.) performed to the P wave duration, QRS complex duration, ST-T complex, QT interval, T wave duration and RR interval were used as temporal features, and mathematical operations performed to ECG signals bounded by the P wave, QRS complex, ST-T complex and QT interval were used as morphological features. A 10-fold Monte Carlo cross-validation was employed to analyze the reproducibility of the classification results by randomly splitting the dataset into training (70%) and test (30%) sets with balanced classes. Mean classification accuracies above 93% were achieved when the SVM classifier uses only temporal ECG features, only morphological ECG features, and both temporal and morphological ECG features. The best classification performance was achieved when temporal and morphological ECG features are jointly considered by the binary SVM classifier (accuracy 96.67%, error rate 3.33%, sensitivity 97.33% and specificity 96.00%).

The aim of this work is to compare the different windows performance in EEG signals related to movement intention, to find the adequate window in specific frequency bands filters. For this purpose, FIR filters using different window functions were implemented in two kinds of signals: a set of simulated signals and a dataset containing EEG movement intention records taken of PhysioNet. The movement intention could be understood as the product of the neuronal synchronization before the movement onset. The similarity of the results was measured using the Euclidean distance. The results obtained suggest the existence of a window function as the most suitable and robust for the EEG movement intention analysis.

The development of innovative cardiac assessment techniques has optimized heart conditions diagnosis, however it has led to a reduction of auscultation practice and hence its effectiveness. Furthermore, in public health systems it is not possible to meet the demand of patients who require the use of these new technologies, so auscultation is key in the process of evaluating cardiac subjects. Based on this situation, this document describes a proposal focused on the analysis and processing of the auscultated heart signal in order to generate a presumptive diagnosis of heart murmurs. The evaluation of the presence of a valvular lesion consisted in the identification of abnormalities in the relationship between the period of contraction and relaxation of the cardiac cycle. During the experimental phase of this work, acceptable results of sensitivity and specificity were obtained, reason why as future work it is planned its development as a tool of low cost to optimize the detection of valvular diseases.

Autonomic control evidenced in both heart rate variability (HRV) and electroencephalographic (EEG) oscillations, reflect the behaviour of the underlying physiological non-stationary dynamical systems. In order to assess the influence of autonomic changes in the performance of an asynchronous brain-computer interface, recurrence analysis of EEG spectral density time series and HRV feature sets were used in binary classifiers to detect rest state from mental calculation state. Results suggest that recurrence indices of HRV might contribute to improve activity episodes detection for BCI control as the highest performance was achieved with power spectral density features of EEG in combination with recurrence HRV features (AUROC $$=0.81\pm 0.07$$ ).

The detection of heartbeats is a fundamental task in the diagnosis of cardiovascular diseases. In a clinical setting, heartbeats are commonly detected from the electrocardiographic (ECG) signal, but it can also be estimated from hemodynamic signals, such as arterial blood pressure (ABP), pulmonary arterial pressure (PAP) and central venous pressure (CVP). In this paper, we conceived three HMM-based detectors that jointly exploit the information from ECG and ABP ( $$ D_{ECG \& ABP}$$ ), ECG and PAP ( $$ D_{ECG \& PAP}$$ ), and ECG and CVP ( $$ D_{ECG \& CVP}$$ ). The HMM-based detectors are based on the comparison of the difference of log-likelihoods of observation of two competing models that learned the dynamics of the presence and absence of a heartbeat, respectively. The detection performances of the bivariate centralized detectors were similar, but the detector that simultaneously considers ECG and ABP provide the best result (sensitivity = 98.91% and positive predictivity = 99.11%). These results agree when a univariate HMM-based detector only considers the ECG signal in the decision process. The centralized bivariate detection approach proposed in this paper make uses of redundant information in the decision process and is particularly useful when one of the signals is too noisy or missing.

Surface electromyography (sEMG) signals offer information on the natural control of muscle contraction but struggle to identify temporal pattern parameters for several degrees of motion of voluntary hand movements. The complex nature of these signals renders the movement prediction task difficult; therefore, feature extraction and selection algorithms are a natural choice to transform time domain data into a new space domain to enhance recognition. The purpose of this work was to conduct an analysis of a former forearm sEMG database to improve a model to classify 15 defined hand movements. A simpler classification model was created from algorithms, such as naive Bayes (NB), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA). Also, novel preprocessing of the EMG signal data was employed and modeled the movement in virtual simulation software. In the preprocessing, outliers were eliminated, and a scatter matrix algorithm was used to transform the data into a new space to increase the differentiation between distinct classes. The processing window was 62.5 ms to generate a classification and integrate one video frame movement. Experiments yielded promising results, achieving a 93.76% recognition rate in an independent test set. The biomechanical wrist model available in OpenSim was completed by adding the missing degrees of freedom of the fingers to simulate the movement generated from the proposed classification model. The sequence of movement was converted to a biomechanical model and constructed into a video object with the potential for real time use.

The aim of this paper was to characterize the Intrinsic Mode Functions (IMFs) of the Electrical Cochlear Response (ECR) obtained by Ensemble Empirical Mode Decomposition. Sample Entropy was used to identify the IMFs related to background noise, Cochlear Implant artefact, and ECR. Finally, Noise Reduction Percentage was used to quantify the decrease of artefact in the ECR reconstruction.

Respiratory rate (RR) is a handy parameter in the clinical field since it allows the timely detection of diverse pathologies. However, RR is currently acquired using expensive devices which are attached to the patients and therefore may be uncomfortable to use. In this paper, we present a method for the estimation of respiratory rate through a non-contact optical method based on a deep learning human pose detector. The proposed method is tested using a database of videos of subjects performing different respiratory maneuvers to obtain the respiratory signal, and the instantaneous respiratory rate automatically. The proposed method obtained a correlation of 0.8 on static breathing maneuvers with respect to the ground truth signal. For the instantaneous respiratory rate, it was observed through a time-frequency analysis, that the obtained signal shares the same frequency bandwidth as that contained in the ground truth, indicating that the information of both signals is on the same frequency band. Our results indicate the feasibility of employing the proposed method for estimation respiratory rate frequency.

Respiratory rate measurement and monitoring allow predicting adverse events on time. However, in clinical settings, it is performed with specialized devices that are always attached to the patient’s body. The objective of this work was to estimate the instantaneous respiratory rate (IRR) and the respiratory movement (RM) using image processing techniques from a video. A smartphone recorded the videos, and the RM was estimated offline. An RM reference signal obtained in the same test with a thermal sensor around the nose and mouth was used to make comparisons with the computed signal. The remote optical estimation was evaluated on a pilot test with four volunteers in three different conditions of movement and breathing. We analyzed the correlation for the RM signals and the mean square error of the IRR between reference and estimated signals, obtaining a value of 0.737 and 0.091, respectively. These results are competitive with the ones founded in the bibliography. Moreover, the respiratory rate estimation has a precision of P = 0.98 and sensitivity S = 0.99, which makes our method superior. This outstanding performance is due to the proposed framework is robust in the presence of motion artifacts, even motion artifact that corrupts the reference signal from the thermal sensor.

Respiration movement and respiration rate have been used to monitor breathing status for diagnosis and fitness purposes. From a given video sequence of a person facing a camera, this system here automatically detects and tracks a region of interest (ROI) on the chest, using the Kanade-Lucas-Tomasi method, after applying the Viola-Jones algorithm and identifying the Harris-Stephens features for tracking the ROI across frames. The displacement in the vertical direction of the ROI (frame by frame) was used to estimate the respiration movement, after low-pass filtering at a proper cutoff frequency. Finally, the respiration rate is estimated from the respiration movement signal by a root MUSIC-based estimator. For the 12 video files provided, we obtained respiration movement signals with correlation indexes respect to the corresponding ‘references’ of above 90%, in most cases, and respiration rate signals with normalized root mean-squared errors with respect to the inst_freq around 10%. A global ranking index of around 0.8 was consistently obtained. Computer vision algorithms are well-suited for estimating respiration movement and respiration rate signals from frontal video sequences.

Mammography is the typical diagnostic test for early detection of breast cancer. The presence of microcalcifications and masses in the images may be an indicator of the disease. The microcalcifications size is very small, so, in many cases, they are not visible from medical images by radiologists. On the other hand, masses can be also undetectable if image contrast is not good enough. The computer-aided detection (CAD) systems are useful tools in facilitating the physician´s diagnosis. The CAD system proposed in this work is aimed at improving image quality based on image processing. On these improved images, it segments the mammary gland and highlights the presence of microcalcifications and masses. The system improves image contrast by means of convolution filters, it also eliminates artifacts by means of morphological opening and closing and Laplacian filtering, and uses entropy-based methods for segmentation of the gland and morphological filtering and histogram readjustment to enhance microcalcifications in the image. Masses are detected using an iterative contrast increase method. The system was tested with an annotated database (DB) MIAS, in oblique lateral views of glandular, glandular-dense and predominantly adipose breasts, which included images of malignant and benign lesions and other breast images without them. The system was evaluated with respect to the DB annotation, for a sample of 115 images. The performance of the system revealed a sensitivity of 93.2%, a specificity of 85.3%, a precision of 90.4% and an accuracy of 92%.

Retinoblastoma is the most common intraocular tumor in childhood. Topotecan has been widely used as an antineoplastic agent for retinoblastoma treatment. Topotecan penetration into living tumorspheres is quantified using confocal microscopy. This fluorescent drug dyes the living tissue and it can be recorded in a sequence of images over a period of time. The effective penetration of the drug depends on culture characteristics and requires a very specific timing. This penetration time is calculated empirically by an expert. The purpose of this work is to offer a statistical model to automatically estimate the penetration time of topotecan in the cell, based on the information obtained from a sequence of tumorsphere images.

Nowadays, several medical procedures depend on the comparison and combination of images obtained in different modalities (magnetic resonance, computed tomography, PET, among others). Image registration is a geometric transformation process to align two or more images. It is necessary to have robust algorithms to find the best parameters of transformation in order to achieve accurate registrations. Reinforcement learning allows to train an agent through direct environment interaction, to achieve a goal. In this work, a comparison of the performance of Q-learning and Deep-Q with its variants is presented. Brain magnetic resonance images are used in 2D domain considering rigid deformations. The comparison is based on the reward values, computing the Pearson correlation factor in monomodal registration and Mutual information in multimodal registration, obtained during the learning process. It is also considered an error measure between the target parameters and the achieved ones. Finally, a backup memory criterion is proposed to train the Q-Network methods. Experimental results show a successfully behavior in all cases, but performance is improved when the proposed criterion is applied.

To be used as intracorporeal temporary orthopedic biomaterial, magnesium alloys have to be biocompatible but also to have an adequate and controlled corrosion rate. It means that its degradation accompanies the formation of bone tissue since it is expected that the material maintains its mechanical properties until it fulfills its function and then degrades in the body. The degradation of AZ91 magnesium alloy generates the release of hydrogen gas. It is important to quantify the hydrogen evolution since it is directly related to the Mg corrosion rate. Hydrogen evolution can be experimentally quantified with high associated errors. Digital image processing tools allow the evaluation frame by frame of the hydrogen release from the surface. In this work, we performed a set of digital image processing operations to determine the gas generation rate in the surface of metallic pieces of AZ91 magnesium alloy with and without a surface oxidation process, captured from an optical microscopy camera. The proposed approach detected gas bubbles in a region of interest and then it measured the present volume in each frame of the video sequence. Surface oxidized AZ91 samples showed a slow rate of hydrogen release at short immersion times, showing a retarding in corrosion or degradation of the sample at early times.

Contouring of organs at risk is an important but time consuming part of radiotherapy treatment planning. This procedure is usually performed by manual contouring and it may take several hours to delineate all structures of interest by radiation oncologists. This work presents an algorithm for automatic delineation of the femoral bone used as tool within Eclipse® software to save time of radiation oncologists. The Eclipse’s script was successfully characterized in pelvic CT Scan DICOM files of prostate patients (n = 11) from two different hospitals, AC Camargo Cancer Center and Sao Paulo Hospital. The automatic delineation was compared with manual and approved delineation through blind test evaluated by a panel of seniors radiation oncologists (n = 9) from both hospitals and the overlapping evaluation was assessed using Dice similarity coefficient. Clinical experts considered that no corrections were needed in 77.8% and 67.8% of manual and automatic delineation respectively. A similarity of 0.94 ± 0.07 was achieved between manual and automatic delineation for the lesser trochanter, 0.91 ± 0.07 for femoral neck, and 0.86 ± 0.08 for femoral head. The results showed that the automated delineation was reproducible in all patient and its performance was similar to manual delineation. In conclusion, the script is capable to relieve the physician of time-consuming tasks and shows the great potential to be employed for automatic segmentation of organs at risks in pelvic or others anatomical body regions.

Image denoising in medical image processing is essential to improve the visual quality of the image and, consequently, the diagnosis. Speckle, a multiplicative noise that degrades the image, is common in ultrasound imaging as it tends to degrade the resolution and contrast in the image. Several filters have been developed to reduce this kind of noise. This paper presents a comparative evaluation of some anisotropic filters used to reduce the speckle noise in ultrasound images. The evaluated filters were Coherence-Enhancing Diffusion (CED), Speckle Reducing Anisotropic Diffusion (SRAD), Detail-Preserving Anisotropic Diffusion (DPAD), and Anisotropic Diffusion Filter with Memory Based on Speckle (ADMSS). The filters processed four phantom ultrasound images with speckle noise. The comparison performances of filters occurred by using Mean Square Error, Signal to Noise Ratio, Peak Signal Noise Ratio, and Structure Similarity Index. The results showed that ADMSS better reduces speckle noises in ultrasound images, compared to the other filters evaluated.

The analysis of locomotion in laboratory rodents allow the study and understand the effects of diseases or conditions that affect the nervous system in the patterns of the rodents using qualitative and quantitative metrics. Many methods for the quantitative analysis of rodents’ locomotion have been previously employed. These methods rely on the use of high-speed video sequence recording that is analyzed through the analysis of the translations of the joints of the extremities of interest along time. However, there does not exist a standard system that allows the acquisition of videos for such type of analysis. In this work, we describe a system and its components for the synchronized acquisition of multiple high-speed video recordings of rodents during locomotion, including the required post-processing image algorithms.

Unsupervised learning methods are commonly used to perform the non-trivial task of uncovering structure in biological data. However, conventional approaches rely on methods that make assumptions about data distribution and reduce the dimensionality of the input data. Here we propose the incorporation of entropy related measures into the process of constructing graph-based representations for biological datasets in order to uncover their inner structure. Experimental results demonstrated the potential of the proposed entropy-based graph data representation to cope with biological applications related to unsupervised learning problems, such as metagenomic binning and neuronal spike sorting, in which it is necessary to organize data into unknown and meaningful groups.

This work presents an evaluation of two machine learning schemes for lid segmentation on meibography images. Both schemes use same input features based on pixel gray levels, laplacian and entropy filters, and also distances to anatomical landmarks like pupil and eye lashes. The methods evaluated were support vector machines (SVM) with 4th degree polynomial kernel, a Neural Network (NN) with 60 neurons distributed on three layers and the intersection of both. Performance was evaluated with AUC on a bootstrapped cross validation (CV) tests of 20 folds. Dataset is conformed by 465 images for each fold entire dataset was split on 70% training and remainder for testing. Results of CV: SVM 0.851 ± 0.103, NN 0.713 ± 0.144 and SVM & NN 0.835 ± 0.118, suggest that SVM is a suitable model to be used for this task.

Mild cognitive impairment (MCI) is an abnormal deterioration of cognitive functions, whose prevalence is considerable in adults older than 65 years old. Several of these cases will convert to Alzheimer’s disease and therefore, MCI’s simple, proper and opportune diagnosis continues to be a research field with great impact in public health. In this paper we propose tortuosity, which is defined as a shape measure that has been applied to quantify morphological changes in several anatomical structures, as a potential biomarker sensitive enough to depict early brain changes that appear in MCI subjects in comparison with healthy controls (HC). Also, a random forest (RF) classification strategy was implemented to discriminate between MCI and HC populations. A training population selected from the ADNI database and a test group of 21 mexican subjects were analyzed. Statistical analysis showed significant differences (p < 0.05) in tortuosity indices determined for MCI vs HC populations in most of the measured cortical structures. Classification rates increased by 6.7% during training and 4.77% during the test stage, when incorporating tortuosity to other image-based features set. This suggests that tortuosity is a promising morphological parameter to be considered for early stages of Alzheimer disease (AD) and that, combined with an RF classifier, it can adequately separate HC and MCI subjects.

Parkinson’s disease (PD) is a neurological disorder that is progressive and causes losses of dopaminergic neurons from the substantia nigra, a region in the human brain. The decrease of dopamine in this area elucidates the presence of motor symptoms, such as tremors, bradykinesia, rigidity, gait impairment, and non-motor symptoms, e.g., depression, loss of cognitive functions, sleep problems, and nerve pain. Among the motor symptoms, tremors can have the most impact on the social activities of people with PD. Furthermore, there is difficulty in diagnosing the underlying disorder that causes tremors. Thus, the study and development of methods to assess tremors and their severity is of paramount relevance for clinical practice. A typical clinical tool to evaluate tremor severity is the analysis of hand drawing shapes (e.g., spirals, circles, meanders, waves). The evaluation of these drawings is dependent on the experience of professionals, yielding a high variability of results. Aiming to contribute to the objective evaluation of hand drawing shapes of people with PD, this research proposes the application of the Random Forest Classifier to classify Histograms of Oriented Gradients (HOG) estimated from sinusoidal patterns collected from healthy individuals (n = 12) and from people with PD (n = 15). The highest accuracy, sensitivity and specificity classification success rates were of 83%, 85% and 81%, respectively. These results can be relevant for the early detection of pathological tremors, the follow-up of medical treatments and the diagnosis of parkinsonian conditions.

Pattern recognition of myoelectric signals for prosthesis control has been extensively studied in research settings; however, its systems perform poorly in the presence of electrode shift, defined as the movement of surface electrodes with respect to the underlying muscles. In this paper, we present the results of using image processing techniques for gesture recognition in the presence of electrode shifts based on High-Density Electromyography (HD-EMG). Here the instantaneous sample of each EMG channel is represented as a pixel of an image that changes with different movements. In this image, various patterns are recognized as associated with specific gestures. We found that feature extraction based on image processing techniques can improve the accuracy of gesture classification from HD-EMG signals in the presence of the electrode shift.

The development of antimalarial drugs requires performing laboratory experiments that include the analysis of blood smears infected with Plasmodium. Analyzing visually the resulting microscopy images is usually a slow and tedious task prone to errors due to fatigue and subjectivity of the analysts. These facts have motivated the creation of digital image processing systems to automate this analysis. In this work a computer vision solution to process microscopy images of blood smears containing erythrocytes infected with Plasmodium is shown. This system performs tasks like illumination and color correction, image segmentation including splitting of clumped objects and extraction of color features. A set of different classifiers was tested and evaluated to find the best one in terms of indexes of effectiveness. A new feature named pixels fraction was introduced and used together with a number of other color-related features, from which a subset to classify cells into normal or infected was selected. The classifiers evaluated were: support vector machines (SVM), K-nearest neighbors (KNN), J48, Random Forest (RF), Naïve Bayes and linear discriminant analysis (LDA). All of them were evaluated in terms of correct classification rate, sensitivity, specificity, F-measure and area under Receiver Operating Characteristic (ROC) curve (AUC). The effectiveness of the pixels fraction as a new feature was demonstrated by the experimental results. In regard to classifiers, J48 and Random Forest showed the best results.

Phase-contrast mammography using synchrotron beams (PCI_SR) has shown quantitatively better results in terms of image contrast and detection of small details with respect to conventional digital techniques. However, this new technique has shown higher levels of random noise than common digital techniques. The objective of this work was to reduce noise in PCI_SR images to levels, similar to those of hospital equipment without significantly affecting the spatial resolution, contrast and visibility of fine details. Five filters: Wiener, Gauss, bilateral and coif and biord wavelets families, were used independently to reduce image noise. Image quality before and after filtering was estimated using objective indexes. The results showed increases in Signal-to-Noise Ratio (SNR) without impairing other quality indexes; which demonstrates the superiority in image quality of this innovative technique for the detection of small lesions such as microcalcifications and low contrast masses.

This work presents the development of an interface to obtain the morphological parameters for the classification of the type of footprint of a person using the Hernandez-Corvo protocol. A digital photograph of the footprint is acquired with Matlab an algorithm which is applied to detect the contour of the footprint. Hernandez-Corvo protocol is applied through the interface to determine the morphological parameters, the distances of internal prominences of the forefoot and hindfoot are measured. The distance of the longest phalange and the extension of the metatarsal is obtained and the type of footprint is classified as flat, high and normal in different degrees. The ease of measurement of morphological parameters for the analysis of the pathology of the patient’s foot is beyond a general study. The interface was tested, applying the protocol to the footprint of a child of 7 years-old which resulted in a percentage of 32.65 corresponds to a flat foot. The work time in the interface was approximately 4 min including to display the parameters of interest.

Cervical cancer is still a significant cause of death, especially in developing countries. The detection and correct treatment of the disease is vital in its early stages. One of the key factors in selecting appropriate treatment is the identification of the cervix type. The objective of this work is to propose a Convolutional Neural Network (CNN) architecture to perform a classification of the cervix from a set of images published by Intel and MobileODT. The proposed architecture is combined with a preprocessing algorithm based on an assembly of color models to select the region of interest in the image. The obtained model provides better results than other models in which transfer learning is used or there is no preprocessing stage.

In this paper we analyzed different techniques, included in the Sperm Motility Tracker (SMT) software, used to determine the standard motility parameters in video sequences of sperm. The commercial Sperm Class Analyzer (SCA) software was used as reference to evaluate the two proposed methods. Two tests were performed: first, samples of ram and buck sperm with a constant frame rate were analyzed and the linear correlation coefficient was found between the variables measured with each method and with the commercial SCA. In the second test, samples of ram sperm were acquired with three different frame rates and the parameters that depend on the smoothing procedure were compared. As a result of both tests, the measurements obtained with the SMT were very similar to the ones obtained with the SCA for the most of variables. As a particular case, one of the proposed methods showed a slightly better performance to measure the amplitude of lateral head oscillation (ALH), but the measures of the beat-cross frequency (BCF) present significant differences regarding the commercial SCA values.

The use and analysis of ultrasound images (US) is one of the safest and most accessible ways to monitor and diagnose illness; however, expert knowledge is required for the interpretation of these images. The aim of this project is to propose a practical and low cost alternative for the visualization of anatomical structures in 3D from 2D B mode US images acquired as a video. In particular, this project focused on the 3D reconstruction of the kidney, which is an organ of great importance in keeping the blood clean and maintaining the body’s chemical balance. Our strategy consisted of acquiring the images with portable US equipment and applying digital image processing techniques such as filtering and segmentation in order, to then perform the 3D reconstruction of the anatomical structure of interest.

Nowadays artificial intelligence has become the iron horse in high-performance computing, solving problems that were impossible 10 years ago. This work uses a deep learning technique named Mask Region-Convolutional Neural Network (Mask R-CNN) using images of nanostructured materials obtained from a transmission electron microscope (TEM) at a nanoscale spatial resolution. In those images, we observed different regions with specific structure correspond to yttrium silicate and silicon oxide materials system. The architecture Mask R-CNN was trained with TEM images, and performs the classification, location, and segmentation of chemical compounds with a data set of 26 images, reaching scores above 90% of accuracy.

Cutaneous leishmaniasis is a parasitic disease transmitted by a vector prevalent in tropical rural areas affecting people with low economic resources. Treatments for this disease have significant adverse effects and a different therapeutic response in patients; therefore, accurate follow-up is necessary. Although there has been an increased interest in generating other options for treatment efficiency, the follow-up does not have standardized quantitative methods that allow an accurate estimation of the area and volume of the ulcer in the different phases of the disease. In this work, the proof of concept of a methodology based on photogrammetry was carried out to perform the quantitative follow-up of the treatment of cutaneous leishmaniasis ulcers in hamsters. For this, several stages were implemented, from the acquisition of videos by an in-house artificial vision system, to the measurement of ulcers using a commercial image processing software.

Diabetes often results in severe ulceration of lower limbs’ tissues, a condition described as diabetic foot. Recent research has shown that therapy combining photobiostimulation and the use of natural latex biomembranes can significantly improve tissue regeneration and promote healing in otherwise difficult to treat diabetic foot wounds. We propose a method using image processing techniques to supervise wound healing during therapy using photobiostimulation and latex biomembranes. The proposed method analyzes visual aspects related to ulcer’s inflammation and tissues regeneration. The algorithms, implemented in Python, are based on digital filters and post-processing techniques that extract the regions of interest (ROIs) and estimate the total ulcer area and tissues characteristics over the treatment sessions. The experimental results suggest that the method can be used to automatically monitor tissue regeneration during the treatment, which can in the future assist the treatment by providing automatic feedback.

In this work, a strategy for the segmentation of Interstitial Lung Diseases (ILD) from a High-Resolution Computed Tomography (HRCT) volumetric image by means of a U-Net convolutional network is presented. In particular, the delimitation of Idiopathic Pulmonary Fibrosis (IPF), Pulmonary Emphysema (PE) and Healthy Lung Tissue (HLT) were carried out. The key idea of the proposed strategy is that the U-Net training was performed using three slices from the HRCT. The results of the segmentation of all tissues at once in the whole volumetric image were: 93.6% of accuracy; 80% for the intersection over union (IoU) metric; and above 0.9 for HLT, around 0.8 for IPF and PE in terms of the DICE similarity coefficient. These results suggest that the proposed approach could be used to properly segment different lung tissues at the same time, using only partial data from the HRCT image instead of a large dataset for the training of the U-Net.

This paper proposes the implementation of the Viola-Jones algorithm for the Optic Disc (OD) localization in eye fundus images. A robust classifier for the detection of the region of interest (ROI), which contains the OD through the Viola-Jones algorithm was trained, and the center of the ROI for the OD localization was found. A 8776-image dataset for training (7300) and testing (1476) the detector was conformed with images from public databases: DRIVE, DRIONS, ROC, HRF, MESSIDOR, DIARETB0 and DIARETB1. A success rate of 99.6% was obtained for the OD localization in an average time of 474 ms. The Viola-Jones algorithm results a fast, accurate and low-computational cost algorithm for the OD localization and it is applicable in the detection and localization of other structures as the macula and fovea. The results are comparable with those of the state-of-the-art algorithms and, in addition, they could be improved in future works and may be applied in eye computer-aided diagnosis or periodic evaluations of the eyes in real-time mobile applications.

The study of the vestibulo-ocular reflex (VOR) has been mainly focused on horizontal responses because it is the plane in which the human being is more accustomed to movements. With the use of sensors (accelerometer and gyroscope) as well as a video camera, the response of the VOR in the vertical plane was recorded. Using image processing techniques, the displacement of the eyeball was obtained and with the sensor (accelerometer and gyroscope) it was possible to record the movement of the head. Tests were performed with pendular movement in the vertical plane to obtain the gain of the vestibulo-ocular reflex. Although it is possible to record the horizontal responses, the tests were planned to register a greater alteration in the vertical plane. It was observed that it is possible to record in the vertical plane the responses of the VOR, which can be very useful in different areas such as sports medicine, aviation medicine for the detection of problems associated with posture and balance.

The detection of biosignals includes the use of biomedical electrodes. As the obtention of biosignals is vital for medical diagnosis, electrodes with good performance must be used. In this paper, electrodes made of Polypyrrole (PPy) and Polyvinyl chloride (PVC) were fabricated in order to record ECG signals. Besides, a methodology of their fabrication is presented as well as a comparison between the ECG signal measured with commercial Ag/AgCl electrodes and PPy/PVC electrodes. The results showed a good performance of the PPy/PVC electrodes compared to the commercial ones, making this type of polymeric electrodes a very reliable tool to acquire any type of biosignal.

Hearing loss is an important health problem. It affects about six per 1,000 live births worldwide. Hence, the implementation of a newborn hearing screening program represents a goal of a good health system. For the effective implementation of an efficient hearing-screening program, appropriate technologies are needed to make objective physiological measurements. Within these types of technologies, the most commonly used are Otoacoustic Emissions (OAE), Automated Auditory Brainstem Response (AABR) and the quite new Auditory Steady State Response (ASSR). This paper presents the NEURONIC INFANTIX Newborn Hearing Screening System for implementing these techniques. The main characteristics of the hardware architecture, its Control Module, Auditory Evoked Potential Record Module and Otoacoustic Emissions Module are depicted. Finally, a prototype and a validation experiment of the AABR technique are shown.

The loss of strength in the hand caused by different pathologies, accidents or injuries, affects the development and performance of the daily activities of the subject and if they are not treated properly they can generate a permanent atrophy in the functionality of the hand. Currently, there are different therapy options and high-cost technological alternatives that focus on the rehabilitation of strength and the coordination of hand movements. Therefore, we propose the development of a prototype technology that measures the strength of the fingers through a modern system, which uses an optical principle sensor, as a low cost alternative, that can be implemented in physical rehabilitation therapies. This to get the strength and coordination of fingers of the hand. The main elements of the prototype are: plastic pieces that go in the fingertips and distal phalanges, palmar coating, a distance sensor (composed of a photoresistor, a led encapsulated in a tube), springs that provide three levels of resistance, a microcontroller and a graphic interface that allows the user and the specialist to know the progress of the patient during therapy.

Chronic obstructive pulmonary disease was the fifth highest cause of death in 2002 and it is projected to be the fourth highest cause of mortality by 2030 [1]. It is a heterogeneous disease, but the basic abnormality in all patients is airflow limitation, a value that could be easily assessed by pulmonary function tests. These tests are not readily available in low and middle income countries. Reasons include high equipment costs and the need for trained personnel. In this work, we present a lung function test that measures three highly relevant values: the forced vital capacity; the forced expiratory volume in one second and their ratio; the Tiffeneau-Pinelli index. The sensor is based on an optical detection principle. Light is coupled into a silicone fiber, that consists of a core and a cladding with low refractive index contrast to ensure high bending sensitivity. All parts of the sensor are low-cost, the silicone fiber can be both disposed and autoclaved.

The aim of this paper is to present the main features of a new approach to cardiovascular screening in newborns. The proposed system is made up of the Recorder and the Analyzer. The former is a medical device able to acquire two channels of pulse oximetry data and lead II from the standard ECG simultaneously. Data transmission to the Analyzer is supported by the Bluetooth standard. The Analyzer is an Android application run in a mobile device to analyze several ECG and plethysmography parameters that allows newborn’s classification as cardiovascular “healthy” and “no healthy”. QRS complexes are detected as the first step to study the cardiac rhythm looking for arrhythmia events. QT interval duration is measured to check if the studied newborn is prone to sudden death. Pulse oximetry values are measure in finger and leg to detect dangerous differences. PWTT index computation offers an indirect estimation of cardiac output; this index is inversely proportional to the cardiac output. A prototype was developed and tested at laboratory level. Results indicate that the developed device is safe according to the IEC 60601-1 standard, and sensitivity in QRS complex detection was 99.36%, enough for the intended use of the proposed system.

In the present study, we present the design, manufacture and analytical performance of a graphene sensor (SLG/SiO2/Si) for the in situ detection of HNO3 as a substitute measure of NO in aqueous conditions. Our electrochemical and FET studies demonstrate that the CVD graphene, which is applied as a sensing platform, can respond rapidly to NO metabolites in physiological environments with a micromolar sensitivity. These results are promising in the development of a sensor that allows real-time monitoring of the physiological levels of NO in cultured cells and plasma.

The current work presented in this paper is focused on the development of a heart rate (HR) monitoring system which allows the coach and/or a physical trainer to monitor in real time the HR of all his athletes while they are in an exercise activity, through an application developed on Android. The system has the possibility of sending the data of one or more athletes to a single module via Zigbee, and then retransmit them using the 3G network to a remote display and processing unit that does not require high-cost equipment. The system consists of a unit of acquisition and transmission of HR, a Gateway, and a monitoring and analysis unit. For the acquisition of the HR, which in a high performance athlete is estimated between 40–195 BPM (beats-per-minute), a chest strap is used including a HR sensor, which is connects to a microcontroller that detects the heartbeat as a High/Low pulse. The microcontroller is responsible for calculating the HR and the packing of the data for transmission to the Zigbee module. The Gateway is the sink node of the network where all the data coming from the different acquisition and transmission units of the HR is received. The Gateway manages the 3G module and the Zigbee module, and a microcontroller establishes the connection with the communications manager program hosted on the remote server and keeps it operative automatically. The mobile device consults the database and, through an interface, graphs of HR behavior are generated during the training.

Diabetes is a huge and growing problem, the costs to society are high and increasing, as well as affecting in a considerable way, the quality of life of diabetic patients. One of the main complications of diabetes is the high probability of developing some kind of neuropathic ulceration, particularly of the feet. Among the most common methods for early detection of ulceration, is without a doubt, the continuous temperature monitoring on the bottom foot area. The goal of this research is the early detection of ulcers development in subjects with diabetes. A device with four temperature sensors was designed to continuously monitor the foot temperature and reduce the likelihood of amputations. Tests were performed using the device in 35 subjects with diabetes, we observed temperature variations on the different plantar regions studied here. We concluded that continuous, independent and simultaneous monitoring is critical, as it can provide preventative information for possible ulceration development.

Pain is considered a world problem; several options to release pain have been used. Although thermal patches are commonly used to release pain, there are not enough information to validate if they produce a real therapeutic effect. The present study was done in order to evaluate the effectiveness of a patch designed to keep a constant and uniform temperature through the treatment. 3D human models were generated to evaluate the thermal distribution generated by the patch located over different regions. The effect over fat, muscle and bones was analyzed. Temperature patch from 40 °C to 48 °C and treatment times from 900 s to 1800 s were included in this study. Results show that the thickness of fatty tissue is strongly related with the penetration depth of heat and the effectiveness of the pain therapy. Patch temperatures over 44 °C are the best option to treat regions even with a thick layer of fatty tissue.

In this work we present the guidelines for the construction of a prototype for general purpose measurements. The goal for this prototype of a virtual instrument is to achieve measurements at macro- or micro-scales, depending on the variables. The measurements will be made through sensors from a personal computer without having to resort to commercial instruments. The system consists of a development platform (Arduino), an adapter to adjust the sensors and a graphical interface. The acquisition card has the specifications required for the correct functioning of the sensors, whereas the adapter consists of different plug-and-play inputs. In our development phase, the regulation of voltage and frequency was tested, as was the communication required for correct readings from the sensors. We obtained a system and an interface capable of recognizing the connected sensors and that showed the measurements in the interface developed in LabVIEW; our system also allowed saving the information (input and output).

In recent years, no other aspect of muscle loading has been more investigated in the literature than eccentric movement. However, issues involving muscle responses from eccentric training are not fully understood. Thus, the objective of this study is to develop and test a new electromyography device (EMG Lavita) capable of assisting researchers and clinicians in understanding the muscular responses from eccentric contraction. An electronic circuit composed by an INA118P, a 30 to 150 Hz bandpass filter and a rectifier was developed. The analog signal is converted into a digital signal using a 10-bit A/D converter that is part of the Atmega328p microcontroller which sends the data through a serial communication to a supervisory system which them processes and shows the data in graphic way. A pilot test was performed and simultaneously recorded the EMG signals from the brachial biceps muscle using two different electromyography devices, a Commercial EMG and EMG Lavita. For this test one researcher performed 5 data collections consisting of 10 repetitions of the elbow flexion movement with one-minute interval between them. After the data collection, a statistical test was performed to verify the Pearson correlation coefficient to compare the RMS values of the devices. The EMG Lavita behaved similarly to commercial EMG, presenting (r = 0.86) which shows a good to excellent correlation. The EMG Lavita device showed strong correlation with the commercially used device.

There are evidences that ultraviolet radiation has an intimate relationship with cataract in the human lens. However, there is no complete proof that ultraviolet radiation is the cause of cataracts or either acceleration of this pathology. This disease is characterized by increased absorption and scattering of light through at the lens which leads to decreased light transmission to the retina, resulting in poor vision, and eventually leading to blindness. The objective of this study is to analyze the spectroscopy of a human lens and different culture media to determine which solution is most suitable for the development of a future experimental protocol for solar irradiation of the lens. A holder to accommodate the lens was developed, since there is no consensus on the best way to manipulate the lens to avoid its opacification. We performed the spectroscopy of a human lens and three different culture media (saline solution, TC199 with phenol-red and TC199 without phenol). The preliminary result of the lens spectroscopy is in agreement with one described in the literature, demonstrating that the lens holder did not interfere in the measurement. In conclusion, the lens holder can be used to avoid lens manipulation and probable opacification. Among the analyzed media, the saline solution proved to be the most adequate because it does not absorb light in the ultraviolet range and therefore can be used in a solar simulator, as a strategy to prevent dryness of the lens. These findings are important for future experiments involving the human lens and solar irradiation to determine if the effects of chronic exposure of the eyes to ultraviolet solar radiation has any relation with cataract development.

This document presents the design and construction of an embedded system to alert the position of the back in order to improve the occupational health of users who remain long in sedentary positions. The proposed system is an alternative to monitor and alert the spine posture of people whose daily activities demand to remain long period of time in a sitting posture, in order to avoid problems in the medium or long term. The wearable system consists of a flexible resistive sensory surface that molds to the shape of the spine in order to measure its position. The sensors are located in a vest that is easy to use to improve the comfort of the user. The system uses a Bluetooth BLE communication to send the data sensed to a mobile device, which monitor and save the position of the user and be able to alert if it is in an incorrect position that can affect their health. In addition, the data is saved in an online database, creating a history of postural compartment of each user. The app also suggests exercise routines such as active pauses to improve worker performance that reduces work fatigue, improves joint mobility, prevents and reduces stress, and reduces the risk of occupational diseases.

This work proposes a non-invasive ultrasonic device for stimulation of the pelvic floor in women suffering from urinary incontinence (UI), which is an extremely common pathology in older women.The device is composed of an oscillator with a frequency range of 1 to 5 MHz, the time of application of the ultrasonic waves is determined by a frequency of 10 to 50 Hz. The pelvic floor is stimulated by the waves generated by the oscillator, together the frequencies have an energy of 15 mJ. The main contribution of this work is the development of a low cost, non-invasive device for the treatment of urinary incontinence.

The continuous measurement of the level of oxygen concentration in blood (SpO2) and the heart rate (HR) may help verify the state of health of the cardiovascular system. SpO2 Alarm is a wearable technological tool that consist of a SpO2 and HR measurement device and a mobile-device application that collects, via Bluetooth, the information obtained from the MAX30102 module once processed through the ATMega328P microcontroller (included in the Arduino Nano board), and allows real-time visualization of both parameters. The SpO2 Alarm was intended to be used by patients with a risk of suffering low levels of SpO2, which then sends an alert with the exact location of the user to a designated contact in case of detecting a low concentration of oxygen in blood. The goal of the SpO2 Alarm is to facilitate the detection and timely medical attention of cardiovascular diseases and emergencies.

Blood pressure is a very important physiological parameter given it provides information about the state of the cardiovascular system. The present work describes the development of an electromechanical device to measure the systolic and diastolic blood pressure in the digital artery. The measurements are based on the onset of the photoplethysmographic pulse signal when the artery is decompressed during the deflation of a neonatal cuff placed on the middle finger. The device consists of an optical sensor, a pressure transducer, an electronic system based on an Arduino development board and a graphical computer interface written in Matlab. To validate our device we used a Portapres® system. Simultaneous pressure measurements made with our device and the Portapres® were continuously recorded from 9 subjects in sitting and standing positions. The systolic (SBPDA) and diastolic (DBPDA) blood pressures measured from the digital artery were analytically compared. Results obtained for the SBPDA yielded R2 values of 0.59 and 0.76, as well as 0.59 and 0.85 for the DBPDA for the sitting and standing positions evaluated. A comparison of SBPDA values using the Bland-Altman plot yielded mean values of µ = 3.88 ± 11.97 mmHg and µ = −0.6 ± 9.59 mmHg for the sitting and standing position, respectively.

ABDOPRE is a device that reduces the abdominal pressure of intensive care patients with intraabdominal hypertension. The controlled reduction following a time protocol is consequence of the application of an external negative pressure inside a vacuum bell affixed to the patient’s abdomen. Originally developed in 2007, ABDOPRE was tested in four intensive care patients with intrabdominal pressures (12 to 15 mmHg). In three of these patients a reduction of 5 mmHg, 3 mmHg and 2 mmHg was accomplished. Afterwards, a new, cheaper, smaller and lighter version of ABDOPRE was developed using an ARDUINO UNO board for the control system and an ARUINO UNO SHIELD for the electrical interface. This version has an optimized Software and includes two bell sizes of 14 and 24 L respectively.

This work shows the design and development of a portable wireless monitoring center that connects to three vital sign monitors, with each providing electrocardiogram (EKG), partial oxygen saturation (SaO2), and breath signals and heart rate, breath rate, and temperature values. Each signal and value is stored in a variable library in each monitor. The monitors are connected to a Wi-Fi network with the WPA2 security protocol generated by the center. The center accesses the variable libraries of each monitor using the target IP direction. Then, the center groups the data into clusters and connects with a PC to deploy the variables in a virtual instrument with a general screen for deploying the EKG and the values of all of the monitors and specific screens for each monitor. For portability, the center’s power source is 8 AA batteries.

Near infrared systems (NIRS) are able to detect hemodynamic changes in the brain cortex and offer several advantages to be considered as a suitable technology to address problems in medicine. The objective of this work was to design a NIRS prototype for monitoring the dorsolateral prefrontal cortex activity related with tobacco addiction. An analysis of the needs of potential users helped us to establish the design of a device with a safe optoelectronic circuit mounted on an adaptable and portable chase (size of 7 cm × 7 cm × 4 cm and weight of 100 g). Our device is capable to acquire two signals (750 nm and 850 nm) continuously while a subject makes arithmetic tasks. Both signals follow a reliable behavior and are in agreement with the reports from the literature. A clinical protocol comparing non-smokers vs smoker subjects is in progress to validate the results of our device.

High blood pressure and its pathologies are among the main diseases suffered by Mexicans. Many people may consider themselves prone by inheritance or lifestyle, so monitoring this biometry could help for early diagnosis. As part of the treatment, hypertensive people must keep a record that the doctor can consult periodically about the behavior of the patient blood pressure. In this work, a prototype that automatically takes blood pressure and communicates the data to a PC through a graphical user interface (GUI) in MATLAB is proposed. The values of systolic and diastolic pressures set from the prototype were compared against measurements using a digital wrist baumanometer, obtaining a percentage error less than 4%. Therefore, it can be concluded that the prototype gives not only precise, but also exact values of blood pressure.

The paper consists of the development of a transcutaneous vagal stimulation device triggered by respiration and responsible for monitoring two physiological signals, the Electrocardiogram (ECG) and the Respiratory Cycle (RC). The device is used for some pathologies, such as Alzheimer, obesity and hypertension. The advantages reached by the device include a non-invasive method for vagal modulation and presents the option to synchronize the stimulation with the exhalation phase of RC. It is described the stages of development, which are Recording and Control Interface using an application for mobile devices, Stimulation System for the electrical pulses generation and Synchronization System to trigger the stimulation with the RC wave. In addition, it is shown the device as wearable equipment, by the use of a costumed t-shirt. The results of this development include a validation of the algorithm for peak detection, the pattern of the pulses generated on System Stimulation confirmed and the device is considered as a promising alternative therapy for certain diseases.

Resuscitators are devices that favor resuscitation in two types of arrests: respiratory and cardiopulmonary. The correct application of resuscitation can save lives and prevent neurological sequelae; however, few people are trained to give this kind of medical attention. In this work, we present the development of a bimodal electronic resuscitator prototype for adult patients focused on automating the process of respiratory and cardiopulmonary ventilation. For the prototype development, the verification of the compatibility of the working range for adult patients and the pump control completion, that determines the insufflation required by the patient, were regarded. The insufflation has a feedback system to measure the pressure that is provided to the patient and correctly regulate it. The action of the pump generates two treatment options depending on the type of procedure that is required. The prototype considered safety parameters to avoid health consequences and assure the security of the patient. The electronic resuscitator prototype is automatic and portable.

This work discusses the design of a new method for the continuous differential record of the variable Arterial Blood Pressure (PSA) for noninvasive method (oscillometric) to be employed during the study of the vasovagal syncope in subjects with problems of peripheral circulation during the tilt test. We analyze the different sections of the proposed system, it includes the selection and characterization of the pressure transducer for the differential measurement of the variable; as well as the blocks of the analog channel (multiplexer, amplifier, and filters) and the ARDUINO ONE platform to achieve the control of the acquisition of the data; the control of the filling and emptying of cuffs and besides the communication with a station of processing and control through an interface developed in C #. The obtained results evidence adequate linearity and frequency response, allowing to the study of the variable PSA during the development of Tilt test.

Alternating current biosusceptometry assembled with anisotropic magnetoresistive sensor (AMR-ACB) is a radiation-free technique that can enable the real-time monitoring of magnetic nanoparticles in biomedical applications. The aim of this study is to assess the capability of a multi-channel AMR-ACB system in detecting maghemite nanoparticles (MN) in concentrations used in therapeutic and diagnostic (theranostic) applications, such as oncology, multimodal anticancer therapy, or magnetic resonance imaging. The axial sensibility of the AMR-ACB system was successfully characterized in a bench-top study using MN in different concentrations and distances. The MN sample was aligned with the detection axis of the AMR-ACB system, and then it was moved at fixed distances from 0 to 16 mm. The test was repeated with different MN concentrations. The results show that the AMR-ACB system is capable of detecting samples of MN with a concentration of 0.3 mg/mL, and the output signal is directly proportional to the MN concentration. The output signal exhibits exponential decaying with distance, where the signal amplitude at 8.5 mm is approximately 10% of the signal at 0 mm. This work shows that the AMR-ACB system has adequate sensibility to be employed in in vivo studies to detect MN in theranostic concentrations as well as demonstrates the potential of this magnetic method as a possible tool for future diagnosis.

During the last decade, organic nanomaterials have emerged as an exciting research area due to their chemical, magnetic, and optical properties. In biomedicine, the possibility of the nanomaterials to produce reactive oxygen species (ROS) and their luminescence properties are used for the diagnostic and treatment of some specific diseases. Photodynamic therapy (PDT) is an unintrusive procedure that involves the photoactivation, with a specific wavelength, of a photosensitizer (PS) to produce ROS, especially singlet oxygen, which is the responsible for provoking damage to precise cells; however, its performance would increase if we developed the photosensitizers as nanoparticles. In this work, we reported the study and fabrication of silica core-shell nanoparticles (SNP). These nanomaterials were manufactured by a microemulsion method using an inert or photoluminescent polymer as a core. The surface of the nanoparticle was functionalized with Thionine colorant, which was previously tested as a photogenerator of ROS. The optical and morphological properties of nanoparticles were evaluated by UV-Vis spectroscopy, photoluminescence, and scanning electron microscopy (SEM). Finally, the generation of singlet oxygen was evaluated using an indirect test, by using as an excitation source, a green laser (532 nm). The results indicate that the photoluminescent SNPs generate singlet oxygen so they could be considered as a PS.

In order to avoid fractures of endodontic instruments within the root canal, rotary endodontic files have been manufactured in nickel titanium alloy, either conventional or with different thermal treatments. However, fatigue fractures continue to occur, preventing root canal cleaning and shaping. In our research we explored the possibility that titanium niobium alloy may be useful in the manufacture of endodontic rotary files due to its resistance to fatigue fracture. Simulations of the fatigue failure were made by means of finite elements in wires with the properties of the proposed alloys and they were validated experimentally finding similar results. Likewise, simulations of fatigue failure were made of models of the F2 file of Protaper series® with the same mechanical properties tested on the wires but only the rotary file composed of nickel titanium alloy was experimentally validated.It was found that the niobium titanium alloy virtual file was more resistant to fatigue failure than the nickel titanium alloy file, therefore with the limitations of the present study, it can be concluded that titanium-niobium could be an alternative material for the manufacture of endodontic files.

Tumor spheroids is a 3D culture of cancer cells. This type of cell culture is a great tool for the evaluation of novel nanomedicine systems and in other areas of biomedical engineering. The main advantage over monolayer cell cultures is the biomimetic microenvironment which is appropriate for recapitulating tumor complexity. However, current tumor spheroids obtention methods require sophisticated and expensive equipment and are time-consuming. It is possible to obtain these tumor spheroids by centrifugation of the suspended cancer cells in round-bottom tubes and using compaction agents, for example agarose, which is a polysaccharide well known for its function of forming gels. Herein, we developed a method for obtaining cancer spheroids varying the centrifugation time and the concentration of agarose. The variation in spheroid size was analyzed. No significant changes were observed in the morphology or in the initial size and growth of the spheroids; except in those obtained with the shortest centrifugation time. The cell viability of spheroids that showed growth as a function of incubation time was evaluated. Viability greater than 80% was presented, however, the cell viability does not grow when the size of the spheroidal tumor increases. This simple and effective method for obtaining in vitro tumors represents a tool to further studies in Nanomedicine systems or the development of new anticancer drugs.

In this work, the synthesis of cross-linked PVA/PVP 10% w/v hydrogels was carried out by using ionizing energy at a dose of 30 kGy from a Co60 source in order to be used as dressings for skin wounds. These hydrogels were characterized by various tests such as swelling, dehydration, surface pH, Fourier Transform Spectroscopy (FTIR), Scanning Electron Microscopy (SEM) and Cytotoxicity. The results of these tests allowed us to evaluate the physical, chemical and biological properties of these and ensure that these hydrogels are a good tool for the treatment of skin burns.

In this work, neural cell interactions with previously fabricated and characterized polylactic acid (PLA) fibrillar scaffolds, randomly- and aligned-oriented, coated with plasma-synthesized polypyrrole (PPPy) and polypyrrole doped with iodine (PPPy/I) were studied by three-dimensional culture of cells from a neural cell line on the scaffolds, analysis of their morphologic response by scanning electron microscopy and their viability by measuring the metabolic activity of the cells directly seeded on the scaffolds. Cells attached to the composite scaffolds and projected filopodia towards neighboring cells and fibers. Randomly-oriented scaffolds allowed for greater cell population on the scaffolds than did the aligned scaffolds. Greater average cell viability, adequate membrane ultrastructure and even neurite-like processes were observed on PPPy- and PPPy/I-coated scaffolds. These results suggest that PPPy/I and aligned PPPy-coated scaffolds provide a permissive substrate for the growth and survival of neural cells, with potential for supporting the processes of neural tissue repair and regeneration.

Characterization of newly formed bone tissue around surface modified Zr implants is one way to determine and quantify the bioactive response of these generated surfaces. The objective of the present work is to evaluate if the surface modification treatment, by the anodizing technique, has an effect on histomorphometric bone tissue parameters such as mass of bone or bone density, in an in vivo model. For this purpose, a clustering algorithm was designed for the segmentation of trabecular bone tissue in histological images. The experimental results show that anodizing treatment at 30 V produce a significant increase in bone volume. This is evident because the number and thickness of trabeculae increase and the separation between them decreases.

A research project focused on skin elasticity evaluation, was started at the request of burn physicians at the Instituto Nacional de Rehabilitación, to collect information on tissue quality and the efficiency of interventions. As data collection progressed, it was sought to transfer the results to the market, allowing diffusion and use. Thus, it was decided to participate in the Nodos Binacionales de Salud program. Although research at the Mexican National Institutes of Health focuses on solving patient health requirements, the question was how these projects can lead to a sustainable success on the market. During the program we follow the Lean Startup methodology that consists in finding potential market by getting out of the building to do interviews; the main tool used was the Business Model Canvas and mentor support on how to validate business hypotheses. Throughout the program we realized that the initial product did not meet the needs of the physicians and it was decided to make a pivot. A new business model focused on injectable cosmetic treatments was developed, due to its economic viability. In this market, the validation of most of the business model and field of development were achieved, through the discovery of a new product soon to be available in the market. Although the decision of not to advance the cosmetic technology was taken, the primary research project, focused on burn patients do continue in progress, with a new understanding of the relevant information for the customers ecosystem.

Developing bioinks that are biocompatible and well-suited with the printing process is crucial for the progress of 3D bioprinting tissue engineering field. Decellularized extracellular matrix (dECM) bioink is considered a promising material for bioprinting applications due to its inherent composition. However, the laboratory protocols for obtaining this bioink involve the use of sodium dodecylsulphate (SDS) detergent which is widely known as an agent that causes lysis of the cell membrane. This study reports the fabrication of decellularized matrix gels and the comparison between the morphology and rheological behavior of the extracellular matrix gels treated with different SDS elimination methods. These dECM gels were treated with PBS, acetone or trichloroacetic acid (TCA). Scanning electron microscopy revealed that the PBS washes and the acetone treatment maintained the porous gel structure. The flow curve of the three gels was also studied and determined that the bioinks has a pseudoplastic behavior and that the viscosity of the TCA treated gel is relatively lower (0.08 Pa·s) than the acetone and PBS treated dECM, 0.2 Pa·s 0.3 Pa·s respectively. Even though the dECM gels treated with acetone and TCA encourages the cell survival, the storage modulus of acetone-treated bioink is above the loss modulus indicating that the microstructure is able to resist the shear stress and hold its conformation.

The advance of the three-dimensional (3D) computational simulation resulted in high-fidelity virtual heart models, which are useful for implementation in medical educational tools. These computational tools help the study of cardiac anatomy and function and are of interest to many medical professionals. As the appearance of the heart surface is relevant for the comprehension of cardiac movements, the aim of the present work was the development of an animation of the external atrial and ventricular walls, during the heartbeat in a virtual 3D heart, according to physiological data. The animation used data extracted from echocardiogram images of healthy human hearts and ECG timing. The results show that displacements of the epicardial walls measured manually and wall displacements generated by the animation were comparable. Thus, the implemented animation of the atrial and ventricular external walls of the 3D virtual heart complied with those of echocardiogram images, being useful for future implementation in cardiac teaching and (or) learning tools.

Mathematical modeling of eye movement contributes to the learning of the oculomotor system and the development of technologies for device control. We propose in the present article a representative model of the different movements and rotation of the eye (adduction, abduction, elevation and descent) taking into account the anatomical properties of the extraocular muscles, strength-elongation relationship, and the action of the agonist muscles and antagonists. An easy to understand model is presented that allows the analysis of applied forces in muscle contraction, the conversion of length of elongation (or shortening) of the muscle, which generates rotation of the eye, and the vector interpretation of the direction in the space of towards where the eye directs the sight. In addition, an interface was made that allows the user to vary the Force applied to the extraocular muscles and obtain the spatial movement of the eye, with the anatomical and physiological considerations of the oculomotor system. The proposed model allows a simplified understanding, compared to other models, of the physiology of the oculomotor system, and its implementation in computational simulation systems results in faster executions with lower computational cost. The implementation of the proposed model will serve in the future in the control of devices or development of technologies that depend on eye movements or that imitate these movements.

Here we present a numerical study of a nonlinear model of HIV dynamics. The model describes the dynamics of T4 lymphocytes cells, HIV virus and opportunistic infection, and includes the effect of HIV treatment. We show that, as the efficacy of the antiretroviral treatment is reduced, the model display a transition from orderly to chaotic dynamics via a period-doubling cascade. The chaotic regime is interrupted by regions of periodic dynamics known as periodic windows.

Participation of Engineering in sciences that affect people’s lives has generated relatively new disciplines. Rehabilitation is the biomedical area that produces most impact. Technological developments help people with some functional losses and encourage them to participate in their societies. We present an alternative mathematical analysis applied to servomotors that drive biomechanical prostheses by using the State Transition Matrix according to modern control criteria. It allows work in state-space where it´s possible to find new information to enrich conclusions of research. Some properties quantification are required to perform necessary movements in agreement to manipulation of control signal variables that must be implemented. Work points placed on the left side of the complex plane, relevant theoretical gains, as well as time response graphs, will be new parametric indicators. Obtained results show the importance of the state transition matrix used as a control tool in functional analysis of drive systems applied to prostheses, as much as it is needed to each particular case, meaning to each individual patient.

The use of simulators that represent characteristics of the physiological system has great utility for students and researchers in the medical field since they allow a better understanding of the dynamics in this complex system. These simulators are also an important tool for performing “in-silico” experiments, which aim to visualize the body’s response to new drugs and treatments. In this context, we present SCHSim, a computational tool with didactic purposes, that mimics the basic behavior of elastic arterial vessels by means of equivalent electrical circuits based on Windkessel models. SCHSim allows the user to interact with the model by changing some parameters of the system: heart rate, peripheral resistance, arterial compliance and blood inertia. As a result, it is possible to visualize graphs referring to the behavior of blood pressure and blood flow, thus demonstrating the dynamics in this type of system. SCHSim has, in addition to the simulation elements, didactic tools, with information about Human Circulatory System (HCS) theory and instructions for using the software. Through these tools, it is expected that the simulator can be used by students in the basic physiology of (HSC) teaching.

This exploratory study aims to evaluate the autonomic cardiovascular effects of the astaxanthin on pulse rate variability (PRV) of chronically stressed mice.We used male CD1 mice that were divided into four groups: l-control (Ct, n = 3), ll-chronic restraint stress (St, n = 4), III-supplemented with astaxanthin (Astx, n = 4) and IV-supplemented and stressed (Astx + St, n = 5). Astaxanthin (4 mg/kg for 21 consecutive days) in the corresponding groups was orally administered. In the last day, the pulse rate signals were recorded by photoplethysmography. One minute of pulse-to-pulse time series (P-P) were constructed for all the groups. The P-P time series were processed to estimate relevant linear and nonlinear PRV parameters. It was found that in stressed mice, astaxanthin produced: (1) decreased heart rate; (2) increased complexity in the P-P time series and (3) increased fractal behavior in the pulse rate fluctuations. These results suggest that astaxanthin may attenuate the autonomic cardiac imbalance produced by chronic stress.

This article analyzes the simplifications made by methods for assessing baroreflex sensitivity in the time domain, and also discuss the reason to apply them since by using other techniques, better results are achieved.The sequence method should be not applied without checking arterial blood pressure excursion. Using a linear model, as the majority of time-domain methods do, the error increases significantly when the pressure range increases. Additionally, with this novel approach, real curve points can be computed by grouping data, and consequently two important parameters like the setpoint and the gain peak can be obtained.

Many Bioinformatics tools, known as p-tools, have been developed to predict the effect of single nucleotide polymorphisms (SNPs) on gene functionality, in an effort to reduce the need for in-vivo assays. However, the large number of p-tools available and the heterogeneity of their output make their selection and comparison difficult. To study the consistency of predictions across p-tools, here we present two indices and test them on five p-tools whose predictions are based on different types of background information. For this test, SNPs from well-known organism Drosophila melanogaster are considered.

As a result of the increasing interest on genomic signal processing, there is the necessity of develop computational software that combines different tools for signal processing and automatic analysis. This paper presents a computational tool for mapping and clustering DNA sequences. Several DNA numerical representations, a feature extraction method, the K-means algorithm and different clustering evaluation metrics were implemented. This software allows to researchers to perform genomic signal analysis through a graphical user interface, without need programming skills. The tool is prepared to increase their capabilities by implementing different algorithms or modules. Also, a comparative analysis of eleven DNA numerical representation is presented. The results show that Electron-ion and Voss present the best performances when clustering genomic signals using K-means. The clusters quality was measure using the ARI metric.

Outlining lesion contours in Breast Ultrasound (BUS) images is an important step in breast cancer diagnosis. Malignant lesions infiltrate the surrounding tissue, generating irregular contours, with spiculations and angulated margins, while benign lesions produce contours with a sharp outline and elliptical shape. In breast imaging, the majority of the existing publications focus on using Convolutional Neural Networks (CNNs) for segmentation and classification of lesions in mammographic images. In this study we propose CNNs with direct acyclic graph (DAG) architecture for breast lesion segmentation in US images. We also compare the performance of these two proposed architectures with a series architecture. The best results were obtained with the DAG architecture. The following mean values were obtained for the metrics used to evaluate the segmented contours: Global accuracy = 9593%; IOU = 87.92%; BF score = 68.77%; and Dice coefficient = 89.11%.

Visual prostheses electrically stimulate nearby neurons to generate artificial vision in patients blinded by retinal degenerative diseases. Despite some notable progress in the generation of small dot-like percepts induced by electrical stimulation, the physical aspects of phosphenes demand a better understanding. This present work is part of the feasibility assessment of the activation area introduced elsewhere used for estimating the size and shape of the phosphenes elicited by electrical stimulation. Electrical stimulation of retinal ganglion cells was simulated in a realistic 3D reconstruction of the retina. To ensure cell activation, the dynamics of the ionic channels in cell were considered. Visual perceptions elicited by stimulating electrodes reported by Klauke et al. 2011 in clinical trials were directly compared with our simulation framework. The wide range of visual responses formerly described by subjects is highly correlated with our simulation-based findings. Prior to device implantation, simulation-based findings of the activation area can be advantageous to comprehend the physical aspects of phosphenes and to develop an electrode array for generating spatially-localized perception of light.

The performance of unsupervised methods for metagenomic binning is often assessed using simulated microbial communities. The lack of well-characterized evaluation protocols and approaches to community construction cognizant of biological realities impedes the rigorous assessment and standardization of the binning process. This work attempted to standardize performance evaluation using benchmark communities constructed according to the genome similarity metric Average Amino Acid identity. This approach allowed us to extend and deepen our previous research on the unsupervised binning of metagenomic sequence fragments based on low-dimensional embeddings of pentamer frequency profiles. Experimental results evidenced our method’s potential for the binning of metagenomic contigs to become an alternative to state-of-the-art methods such as MetaCluster 3.0.

This paper describes a computational approach designed to study ventricular tachyarrhythmias arising from acute myocardial ischemia. Since these cardiac disorders can frequently lead to sudden cardiac death, understanding their mechanisms is key to improving their diagnosis and therapy. The use of computational simulations based on multiscale mathematical modeling has proved to be a powerful tool in unraveling the causes of this phenomenon. In the first part of this work, we reformulated a model of the ischemic human ventricular cell to simulate the features of the action potentials during acute ischemia. We then incorporated the model into an electrophysiologically-detailed three-dimensional virtual human heart, which was able to reproduce the typical ventricular tachyarrhythmias in the first 15 min of ischemia with rhythms resembling ventricular fibrillation. The results suggest that the extracellular potassium concentration and the presence of a wash-out subendocardial zone are key factors in the vulnerability of the ischemic myocardium to arrhythmias.

Dental caries and diabetes mellitus, represent two chronic diseases of public health that present statistics of high prevalence worldwide. One of the main problems that affect these conditions is demography, since the least developed countries are those that have less access to public health services and are limited by the economic costs that represent the treatments. In this work is presented the univariate analysis, of a set of demographic determinants obtained from the National Health and Nutrition Examination Surveys (NHANES), in order to identify those that present the most significant information related to estimation of these diseases. According to the results, the age of the subjects is the main determinant describing the simultaneous presence of these diseases, obtaining an area under the curve (AUC) value $$\ge $$ 0.846, concluding that age can be used as tool for their preventive diagnosis.

One of the biggest concerns in metabolomic data analysis is adopting good predictive models in cases of asymmetric databases and few samples. As a result, more studies should evaluate statistical methods that can produce adequate analyses despite those limitations. Support vector machines (SVMs) can be an alternative. This study assesses the behavior of SVMs and the application of different kernels in order to propose a strategy for biomarker recognition in cases of organochlorine exposure.The database used in this study comes from an original experiment previously published, in which a HepG2 cell line was exposed to four different organochlorine pesticides, a mixture, and a control. The database consists of 153 identified and 246 unidentified metabolites. First, a partial least squares discriminant analysis (PLS-DA) was applied to the database to obtain the top ten candidate biomarker metabolites. Subsequently, an SVMs algorithm was implemented using four different kernels: linear, sigmoid, polynomial, and radial basis function (RBF). Afterward, a methodology based on recursive feature elimination (SVM-RFE) was adopted in order to obtain the top ten metabolites. Finally, PLS-DA and SVMs were compared.It was demonstrated that SVMs have a good predictive power when they are trained with few samples. The sigmoid and linear kernels with hard and soft margin showed the best performance. Moreover, the SVM-RFE strategy can rapidly identify candidate biomarker metabolites using a limited number of training samples.

The type-2 diabetes (T2D) is a multifactorial chronic disease that reduces the quality of lifestyle and produces the death of a large percentage of the population worldwide. Before the development of T2D a series of symptoms are presented even years before T2D diagnosis. This condition that appears before the development of T2D is called prediabetes. Prediabetes and T2D are diagnosed from the oral glucose tolerance test (OGTT). The OGTT consists in the measurement of glucose and insulin in five-time intervals, the first after 8 h of fasting (0 min) and the other four measurements after taking 75 g of oral glucose in 30-minutes intervals (30, 60, 90 and 120 min). Some parameters have been used to improve the efficiency in the diagnosis of prediabetes and T2D, for example: the area under the glucose (AUCG) and insulin (AUCI) curve during OGTT has been used as a parameter for the diagnosis of prediabetes, T2D and obesity. The aim of this study is to assess the k-means clustering algorithm in the classification of subjects with prediabetes and T2D using the AUCG and AUCI. A database of 188 subjects (male = 88 subjects, age = 42.11 ± 14.91 years old) with values of plasma glucose and insulin during OGTT was used. The k-means clustering performed for AUCG presents acceptable results since the silhouette coefficient is above 0.6 in all cases. The findings in this study indicate that the k-means applied in the AUCG classify subjects with T2D, prediabetes and control. Furthermore, it could even predict those subjects with high probabilities of developing T2D.

Temperature is one of the most common indicators of the structural health of devices and components. In recent times, infrared thermography has become a mature and widely accepted monitoring technique to measure temperatures in real time in a non-contact manner. In the clinical context, it is also used to study some diseases or the mechanical behavior of endoprostheses. However, the assessment of exoprostheses has been limited to gait analysis or the study of their interaction with residual limbs. The aim of this work is to assess thermography as a tool for evaluating mechanical stresses in a transtibial prosthesis through the development of a protocol that allows the identification of critical points. A transtibial prosthesis was recorded undergoing mechanical stress during the stance phase using a device that simulates the human gait and a thermal camera. A protocol was developed to detect critical thermal points that could reveal areas with the greatest concentration of such stress in a gait test. It can be concluded that thermography can be used as tool for assessing lower limb prostheses in dynamic tests because it is a non-invasive method and its results can be obtained in real time.

Acquiring a sedentary posture to develop diary activities is considered like an usual activity in the professional or the educational field. However, a lot of people do not know that this posture causes a high pressure between the seat and the person, which is associated with problems and pain in the low back. This work presents the analysis of the pressure variation between the chair and the user during an hour of undergraduate class; with the goal to provide quantitative information to strengthen the importance of active pauses during a class. To measure, monitor, and save the pressure levels generated, we developed an electronic system using five force sensors. The tests were carried out in the Universidad Politécnica Salesiana (Cuenca-Ecuador) with 10 undergraduate volunteers during 50 ± 6 min. The registered pressure levels were visualized through a graphic user interface in Matlab. The higher pressures were registered on the ischial tuberosities with an average pressure of 16.31 kPa without a backrest and 8.89 kPa with a backrest. In addition, it was developed a finite elements simulation to estimate the contact pressure levels generated between the ischial tuberosity and the muscle tissue, with a maximum pressure calculated of 1.27 MPa, distributed over a 4.49 cm $$^{2}$$ area. The objective of this study is to measure the pressures generated between the chair and the buttocks and to quantitatively characterize the sedentary posture of undergraduate students.

With the aim to determine the importance of considering the contact between bodies, in this work, has been developed a numerical three dimensional model of the foot, which includes 28 bones, 504 thin ligaments, 48 plantar ligaments and the posterior tibial tendon; to find the values of the pinball region contact between each pair of bones, which at the same time, helps to achieve a correct distribution of body weight and, thus, highlight the importance of taking into account the configuration of the contact parameters between two bodies, to perform a correct characterization of the biomechanical effect of the foot. First, the validation of the model was carried out, determining the reaction forces of the metatarsals with the ground, and comparing them with the experimental reaction forces, subsequently, this validation was used to compare the variation of the reaction force once the contacts were modified. It was observed that by varying the value of the pinball region the reaction forces with the ground (GRF) can achieve significant changes (up to 159% in the fifth metatarsal into this work) and generates a wrong or a better prediction.

Hallux valgus and hallux rigidus are the most common pathologies of the first metatarsophalangeal joint. Despite advances in the treatment of hallux valgus and hallux rigidus, no one technique can address the various stages of these pathologies. To alleviate these pathologies there are destructive techniques such as cheilectomies, arthrodesis and arthroplasties, and there are non-destructive techniques such as certain osteotomies. Hemiarthroplasty in the first metatarsal bone allows to recover the mobility of the first ray as well as to eliminate the pain caused by hallux valgus and hallux rigidus. However, there are no studies that allow us to know the biomechanical effect that the implant researched in this article produces on the human foot. Finite element analysis is a good technique that allows to understand these biomechanical effects. The objective of this work is to make a biomechanical evaluation of the HemiCAP®Toe DF by Arthrosurface and in that way to know the effects that this type of devices produces in the foot. To do this a finite element model of the foot after having performed a hemiarthroplasty in the first metatarsal bone was developed. It was detected a loss of capacity to support load in the first ray after introducing the implant, as well as an increase of principal stresses in the second and third ray. Effects of the hemiarthroplasty might cause postoperative medical complications. It was found loss of passive windlass mechanism in the model, leading to a loss of effectiveness in walking.

The risk of fixation loosening by the pedicle failure during or after the screw insertion surgery is high, causing fracture of the bone tissue due to the high stress concentration. The purpose of this paper is study the influence of osteoporosis on the bone-screw interface, based on a 3D biomechanical model of the lumbar section. The Finite Element (FE) method was used. Mechanical properties in health vertebra (HV) and osteoporotic vertebra (OV) were defined. A compression load of 500 N and a moment of 7.5 Nm were assumed in four load scenarios: compression, flexion, flexion-extension and axial rotation. The bone-screw interaction zone and the posterior of vertebra and pedicles were susceptible to bone tissue failure, because the higher equivalent stress were close to bone failure stress. The HV stresses were higher than OV stresses. The higher stress was 8.83 MPa. Opposite to stresses results, the strains were higher in OV than in HV, being the OV strain more than 2 times higher than HV strain.

The purpose of the paper is to study the biomechanics of sickle weeding, thus this study becomes an analytical tool for understanding the causes of injuries. Ten people with experience in sickle weeding were studied. Photogrammetry was used to define the start and end of the movement cycle under study, and accelerometry was also used to obtain the dynamic behavior of the 3D movement for sickle weeding. Accelerations recorded in the neck, cervical, trunk, and lumbar regions are characterized by movements that begin with slight oscillations. The curve moved up to reach two peaks of acceleration approximately at the 25% and the 80% of the cycle. The average acceleration for these points was fenced to 1.3 g. The highest acceleration was at the lumbar vertebra. Farmers took a bending position with the back arched up most of the time during weeding. That position provoked compression at the front of the intervertebral discs and that configuration combined with the time that they remained in this position could be an important factor in the development of low back pain.

In Mexico, transhumeral amputation occurs due to traumatic etiology and represents 4% of all the amputations. These amputations occur during an active period of life, which limits work options and increase user demotivation. It is known that pre-prosthetic and post-prosthetic rehabilitation provides the necessary tools to improve range of motion, increase muscle strength and restore the sense of control to perform activities of daily living. For this reason, virtual therapy environments have been developed for myoelectric prostheses, leaving aside body-powered prostheses. This paper presents the design and development of virtual training system for body-powered transhumeral prostheses in pre-prosthetic therapy. The created device has allowed to efficiently simulate the control of the body-powered transhumeral prosthesis for the user and provides a tool with specific routines of occupational therapy to the therapist. Thus, this provide the opportunity to increase the acceptance of a prosthesis at a higher level.

An experimental analysis is presented in order to assess the best conditions for energy harvesting from the movement of the fingers using a glove-shape wearable system. Polyvinylidene Fluoride (PVDF) piezofilms were used as piezoelectric generators; they were mounted on the glove so they coincide with different interphalangeal joints on both sides of each finger of the right hand. Two scenarios have been carried out for five minutes each and the root-mean-square (rms) voltage of each piezofilm was measured. Scenario 1 consisted in the use of a computer mouse for browsing different web-sites on the internet; scenario 2 consisted in the use of a keyboard to write a text of 190 words. Scenario 2 produced the greater voltage because the number of keys depressed was higher in comparison with the number of clicks on the computer mouse. In the Scenario 1, the greater voltage was obtained from the piezofilm located on the thumb which is a finger not involved with the click action when using a computer mouse so, in this case, it is possible to harvester energy from three fingers instead of two, as have been reported in previous studies. From the results obtained in both scenarios, the best location to place the piezofilms to increase energy harvesting is the posterior side of each finger, specifically on the proximal interphalangeal and metacarpophalangeal joints. Placing the piezofilms on the fingertips did not produce an increase in the generated voltage.

Currently, most finite element models of the foot in literature have simplifications, mainly in terms of real and complete modeling of soft tissues and their proper transmission of the force, causing limitations in the response of the model. A deficient transmission of the force among muscles, tendons and plantar fascia occurs due to unions with other tissues or excessive displacement. These kinds of problems can be fixed, doing the separation among tissues and applying appropriated contacts or displacement constraint conditions.Therefore, the objective of this study is to evaluate changes in plantar pressure in a three-dimensional model of finite elements of a foot in the second rocker of gait when considering the relative displacement and load transmission between muscles, tendons and plantar fascia.Through scanning and 3D processing, the geometries of the tissues were obtained. First-order tetrahedral elements were used for meshing and were considered models of linear elastic and hyperelastic material. The fixed connections among tissues were separated, and in some of these areas, contacts were simulated, allowing a relative sliding of the tissues and proper transmission of force.Results obtained into this work showed a maximum contact pressure localized under the calcaneus of 0.1925 MPa for the sliding contact model (SCM) and 0.1710 MPa for the non-sliding contact model (N-SCM). These numerical values mean a variation of the plantar pressure between both models of up to 12.57%.Analysis suggests that including sliding contacts on the foot model had a significant improvement in the qualitative plantar pressure response.

This paper presents and assesses a case study regarding to Boccia, which in present has been constituted in a Paralympic sport that integrates athletes with different degrees of disability and that has aroused a remarkable interest during the last decade. However, according to the topic review from a biomechanical perspective, it has been detected that information about the movements of assistants or coaches is scarce, which is identified as an important information gap, specifically for the BC3 category of said sport. This is why the goal is to develop a biomechanical analysis of the movements of a Boccia coach/assistant during the ramp configuration tasks. The development of the present study consists of four periods: characterization of the coach, positioning of markers under a protocol of upper limbs, 3D motion capture and biomechanical analysis. The results suggest that due to the nature of the movements developed by the coach, he is at high risk of worsening different injuries already existing in his body, and the ergonomic difficulties of the equipment used for the practice of this sport also plays a role in these issues.

From the design of a Foot Base [1] (FB), in which a Smartphone (SP) connected to a computer application [2] is used, the movements of inversion, eversion, plantar flexion and dorsiflexion of the ankle are measured. Two types of (SP) are validated, one of medium range and another of low range, to know the precision that they have when measuring the angle of the ankle in the stages of the application. The measurement is made with a high precision system, composed of eight cameras (OPTITRAC® flex-13 from Natural Point Inc.) and simultaneously recording the angle measured by the SP accelerometers, yielding the precisions reported in this document. This validation will allow proposing a measurement scheme in the control of the ankle without load.

The pathologies and injuries of the knee are a common problem in the clinical field. There are numerous tests that allow to evaluate in a standardized way the results of the rehabilitation, nevertheless in the medical center where the present work was developed, the evaluation was carried out by treating clinicians without following protocolized and documented criteria. For this reason, a protocol was developed that consisted of a first part that uses the IKDC test and a conventional videographic method to obtain spatio-temporal parameters. The protocol was applied for six patients attending the rehabilitation centre, the indices proposed by the IKDC test and the spatio-temporal parameters were calculated: step time, stance time, cadence and symmetry. It was concluded that the IKDC test provided useful information that allowed recording the evolution in the rehabilitation, to document it, and to complement the patient’s clinical history. Spatio-temporal parameters, on the other hand, did not provide more information for the patients studied. However, the information provided by these parameters contributed tools to quantify the qualitative observations made in patients by treating physicians. Finally, a survey was carried out to the personnel of the institution to evaluate the proposed protocol, which resulted in the decision to implement it routinely for the assessment of the patients of the institution.

Visualization and kinematic analysis of gait with computer programs and quantitative metrics are nowadays essential tools for the characterization of normal and pathological patterns, as well as for the diagnosis and rehabilitation in gait alterations. However, most of the software that is used for the acquisition systems in the gait labs, estimates a predetermined and restricted number of parameters, conditioning and limiting the work of the medical specialists, either in follow-up studies of individual cases or in the gait description between populations. The objective in this work, is to implement a computational tool that accesses the records of the databases generated by gait analysis equipment, for the visualization and obtaining of quantitative metrics from these kinematic studies, facilitating in this way, the identification of normal and pathological gait patterns, as well as facilitate the statistical estimation inter- and intra-patient in the development of comparative studies.

It has been found several cases of anterior cavus foot. Because of that, it is needed the manufacturing of the costume-made orthoses for the plantar foot region and with this the musculoskeletal deformation of the foot can be corrected, reducing the symptom and/or helping the structure.In order to make sure that the insoles were working properly, a survey was performed in a sample of 60 people, from the participants just six of them did not continue wearing the insoles due to different personal reasons. The study did not consider those participants who did not wear the insoles. All persons who participated in the survey and those who wore the insoles for more than one month evidenced positive results in the foot structure.

In this paper is presented the manufacturing process of an Ankle-Foot Orthosis (AFO) model for the treatment of foot drop. To recreate the model were used three-dimensional (3D) printing, three-dimensional (3D) scanning techniques and a computer-aided design (CAD) software. The final result was a prototype Ankle-Foot Orthosis for foot drop patient.

Brain development occurs before and after birth, it is important to determine if an infant would present a developmental disorder such as cerebral palsy. If the case, it is important to start rehabilitation and get to the fullest functional capacity they could at young age. There are different methods to determine a possible developmental disorder such as brain imaging and clinical assessments. One of the methods that has resulted in positive outcomes is based on the spontaneous movements presented by the infants known as General Movements, one of the problems with this method is that is based on the clinician expertise. 3D motion analysis is a technology that allow us to evaluate any movement on all their axis and obtain kinematic characteristics such as velocity, acceleration and smoothness. In this work we proposed a biomechanical model of the full body of the new born to determine if we could study the movement pattern characteristic presented on young infants, we only presented the circular movement pattern on both feet created by a full-term 2-month-old during 5 min obtaining similarities on smoothness, and differences on velocity and acceleration. The results obtained on this study encourage us to create a pilot study in order to assess the general movements presented on infants and to determine the specific characteristics of these movements, with the future aim to evaluate pre-term infants and know if there is a possible developmental disorder.

The biomechanical characterization of Urinary Bladder is essential to create new therapeutic tools as artificial tissue replacement or electrical stimulators. In this paper, we applied a linear model, the so-called viscoelastic Voigt’s model to calculate the viscosity and elasticity in three regions of a bladder under the experimental condition of tensile stresses. Additionally, we acquired the tridimensional surface of urinary bladders by a cloud of points that were processed in digital format. Viscosity and elasticity are represented on the surface reconstruction. Three bovine bladders were processed with an average sample viscosity of 89.33 × 10−6 Ns/mm2 and average elasticity of 1.3 × 10−3 N/mm, the isotropic condition was evaluated. The digital representation includes a cloud of points of 9 × 106 elements on average. These results are the base of a future computational simulation platform.

Human gait data, as well as other biological signals, follow distinguishable and measurable patterns that are important for the evaluation and analysis of movement. In this paper, we used spatiotemporal gait data for classification of lower limb amputee groups (transtibial and transfemoral) and individuals without amputation (control). The classification was made using algorithms based on machine learning, KNN (K-nearest neighbors) and RF (random forest). Three treadmill walking conditions were analyzed: horizontal (0º), uphill (+8%) and downhill (–8%). These conditions were important to establish in which scenarios the data are more discriminating. The classification of the data with all conditions together provided an accuracy of 75.8% and 77.7% for KNN and RF, respectively. The best classification result was obtained using the RF algorithm with the data in the downhill condition, indicating that this condition imposes greater motor demands to the participants, showing greater differences between the extracted variables.

In this paper, a 3D lower limb musculoskeletal model and simulation of multiple sclerosis disease is presented. The Model was developed using the Musculoskeletal Modeling Software (MSMS), MSMS has the advantage that the model can be exported directly to Simulink allowing us to generate Functional Electrical Stimulation (FES) and evaluate different injuries. From the simulations, is possible to obtain the joint range of motion, joint torque, muscle-tendon length, force and moment arm, this is important not only to perform biomechanical analysis but also to design exoskeleton robots for rehabilitation and to generate reference trajectories for control purposes. In order to validate the results, a study case of a normal and pathological gait is presented, then, the results are compared with the literature and with real data obtained from a low cost, and a professional gait capture system.

The aim of the present study was to analyze segments coordination and coordination variability during gait of fallers and non-fallers older adults at different speeds (preferred walking speed (PWS), 120% of PWS and 80% of PWS) through vector coding technique (VC). Thirty-one young adults, 22 non-fallers and 17 fallers older adults participated in the study. All participants practice exercise regularly at least three times a week. They performed a protocol of three 1-min walking on a treadmill at each speed for data collection, in a randomized order. For thigh-leg segments pair, angles were computed during four gait phases (first double support, single support, second double support and swing phase). Data was exported and analyzed with a custom MatLab code (R2018a, MathWorks, Natick, MA). There were significant differences in thigh-leg segments pair coordination pattern, with the greatest differences observed at 80% and 120% PWS for all groups, with emphasis on the older adults groups.

Various studies describe the postural response and ocular movements produced by transmastoid galvanic vestibular stimulation (GVS). To take advantage of this response in a noninvasive vestibular prosthesis, a unilateral configuration would be a practical design for users. However, the locomotor response to unilateral GVS on physiological functions related to the vestibular system has not been thoroughly described. To determine the postural effects of unilateral GVS, sine, square and triangle waveforms of 0.3, 0.6 and 0.9 Hz were used to transcranially stimulate the vestibular system of 12 healthy subjects (0.4–2.3 mA). Center of pressure (COP) oscillations were recorded with a stabilometric platform. The largest mean COP trajectory length in the mediolateral axis was produced while stimulating with a sine waveform of 0.3 Hz, followed by stimulation with a square waveform of 0.3 Hz. Oscillation of COP followed stimulation frequency in the mediolateral axis when a sine waveform of 0.6 Hz was used. Root means square (RMS) value and the area of an ellipse calculated from COP displacement recordings, decreased with increasing frequency when stimulating with square and sine waveforms. No changes in these variables were observed when using triangle (symmetric) waveforms. Amplitude of spectral analysis increased notoriously for sine wave stimulation of 0.3 Hz. In future studies, a larger range of frequencies with square and sine waveforms, may be used to study postural response in relation to stimulation frequency. Results suggest that it is possible to include low frequency square and sine waveforms with unilateral GVS in a noninvasive vestibular prosthesis.

Five hundred years after his death, the figure of Leonardo da Vinci continues transmitting his tireless desire to know and learn. Leonardo is the symbol of a century in which progress impacted, shattering the thickness of dogmas. In the Quattrocento, the doors were definitely opened, ideas spread and still feed us, clear our path and enlighten us. Florence, in Leonardo’s time, was the Silicon Valley of the Renaissance. Leonardo studied the dynamics of water flow in rivers, using colors to show the flow patterns, thus defining the continuous stress on the side walls of the river. He determined, with different colors, the flow characteristics in the center and near the edges of the rivers and extrapolated those findings to the blood that flows in the arteries. Leonardo studied the coronary artery and veins, heart and bronchia in detail and made several assumptions about the cause of atherosclerosis, based on his previous hydrodynamic studies of water flow. Leonardo theorized that diseases were derived from some imperfection in the structure of the human body and addressed the issue of atherosclerosis and its correlation with aging. He accurately described a case of portal hypertension with liver cirrhosis as well as pulmonary circulation and chronic obstructive pulmonary disease. Leonardo was the great innovator in Biomechanics of the cardiovascular system: heart, lungs and circulation.

Three-dimensional printed prostheses have gain popularity due to their cost, customization capabilities and rapid prototyping, which allows researchers to implement over them various control and classification techniques that make them more functional for the users. This paper presents the design, construction and instrumentation of an electromyographic (EMG) active upper limb prosthesis, with a set of distributed PD controllers that ensures tracking of trajectories corresponding to different arm movements. The proposed rehabilitation device employs an artificial neural network to exert the classification of EMG signals which drive the activity of the prosthesis. The classifier defines the reference trajectories which must be tracked by the PD controllers. Finally, the complete integration of the system is presented, as well as the results obtained by each of its parts.

Surgical and medical robotics had become an interesting field of research joining disciplines such as biomedical engineering, mechatronics and automatic control. One of the main design issues of surgical robots is the interaction (contact force) between the end effector and the biological tissue. This manuscript presents the design, instrumentation and control of a surgical robot prototype (SRP) with two robotic arms of three degrees of freedom (DoF) attached to a cartesian robot, getting a total of five DoF per arm. The end effector consists of a robotic scalpel instrumented with force sensors to interact with the biological tissue resulting in more precise cuttings and avoiding, at the same time, damage of surrounding areas. An output-feedback proportional derivative hybrid controller is applied to track the trajectories aimed to realized precise cut. Simulations and experimental results show the performance of the SRP.

Amputation is a physical deficiency affecting indiscriminately the world population, representing a high cost for social security systems in health, in order to improve the life quality of amputees, is necessary obtain prosthesis that simulate the movement of the hand as naturally as possible. This paper, shows a methodology for the continuous and simultaneous control of 3 degrees of freedom (DoF) of the hand, for further applications on prostheses control. The position of the flexion/extension of the wrist (DoF1), Metacarpophalangeal (MCP) joint for tri-digital grasp (DoF2) and penta-digital grasp (DoF3) were estimated, based on surface Electromyography (sEMG) and Support Vector Regression (SVR), using time domain features widely applied in the processing of sEMG signals, like the Hudgin’s feature set. Intersubject and Intrasubject model were assessed, finding that Intrasubjects models had the best performance with R $$^2$$ of 0.73, 0.49 and 0.51 for DoF1, DoF2 and DoF3, respectively. Our approach was able to estimate the target motion profile within $$\mathbf {~22.5^{\circ }}$$ with a low memory usage and minimum computational complexity.

Nowadays breast cancer is one of the main problems that affect women worldwide. In Mexico, this disease is considered the second cause of death of productive age women, and the first cause of death worldwide. This death rate, as well as the incidence, have been ascending in the last years; however, the correct interpretation of test analysis could play a key part in the process. Biopsy procedures are successful only for 78% of cases, which leaves 22% of patients in danger of receiving wrong diagnoses which include false negatives, leading towards late treatment and therefore fewer survival possibilities. This project aims to increase the successful biopsy results by implementing the usage of biomedical robotics. The development consists of a Delta type robot, equipped with infrared sensors for cancerous breast pathology localization, trucut type biopsy needle, thermal image projection, artificial intelligence for optimum trajectory and positioning over the patient. This equipment offers a live detection with infrared technology and a precise incision controlled by the health professional. Therefore, this system offers a more accurate precision reaching the tumour, achieving a better extirpation of suspicious tissue for histopathological analysis, reducing the number of false negatives and, therefore, acting directly over the death rate.

Currently, exoskeletons can be categorized according to the part of the human body they are focused on, their purpose and the type of mechanisms used for their operation. This paper presents the development of an exoskeleton to assist lower-limb motion during human gait and uses the electromyographic (EMG) signals as data acquisition and control system. In addition, the system has an anthropomorphic and adjustable design to serve a larger number of patients and thus make it affordable for a larger sector of the population. In order to verify the suitable operation of the exoskeletal system, experimental tests were carried out with a volunteer and some of the obtained results are presented.

This paper shows the mechanical design by additive manufacturing (DFAM) of a robotic hand with 5 fingers, where the little finger is replaced with a thumb. The aim of this work is trying to look for a mechanical perspective, in order to develop a functional prototype that allows us to visualize and identify all the advantages that we could get with an object’s grip through a hand with two thumb fingers. Also, we pretend since the prototype manufacturing by Fused Deposition Modeling (FDM) show the design technique application based in divergent and convergent remix on digital models of robotic hands like Inmoov and Parloma. Several prototypes were made to obtain as a result robotic hand called Kool, which has advantages of mechanical design, from weight, shape, and cost compared to previous prototypes.

Transcranial magnetic stimulation can be used as a tool for assessment of corticospinal tract integrity by extracting features from elicited motor evoked potentials (MEPs). However, the selection of MEPs from recordings that present them, and the computation of amplitude and latency are usually performed manually. A methodology based on computing summed power calculated by means of the Discrete Fourier Transform in 10-ms windows was used for automatization of this process and compared to manual measurements. Results showed that MEP selection coincided in 69% of cases, and amplitude and latency had average differences of 7% between methods. Therefore, automatization of MEP computations is feasible using frequency-based features.

An important application for Brain-Computer Interfaces (BCI) is using them for spelling task in subjects with severe motor disabilities. Most BCI spellers are based on the matrix proposed by Farwell and Donchin [1] or variations of these. Several defects of those, were resolved by other authors. In this project, an improvement of the regions-based matrix of Fazel-Rezai [2] is proposed to better realize the user experience and the speller performance. This is a preliminary design of a region-based speller using BCI to record electroencephalographic (EEG) signals, taking in mind Locked-in patients. We developed a computer based system which generate a two levels stimulation matrix. The first level has ten (10) regions with alphabet characters, numbers, some short words and a region with the capacity of propose words or phrases, in base of previous letter or words spelled by the user. The second level has only six symbols or words/phrases belonging to the region selected by the patient in the first stimulation level. The Application Module of the system, designed to detect P300 signal, was developed using the BCI2000 software [3] and the framework BCPy2000 [4]. The results of preliminary in vivo trials showed that the enhanced matrix can elicit P300 and the recognition module exhibited an average accuracy in characters’ identification, ranging 75–89% and reaching 100% in one of the trials.

This paper proposes a Real-Time Home Devices Control System based on the processing of the user’s pupil position. A low-cost method is proposed to assist people with reduced mobility, unable to handle any type of device manually. The system was tested using a television and the software “Plaphoons” as test devices. It is capable of identifying up to six different action commands. The method allows to identify if the user is looking towards the screen and uses this information to determine if you want to perform some command. Each time a command is identified, a signal is sent to an electronic interface that allows to operate a universal remote control adapted for that purpose.The system was successfully tested in real cases by patients of the Cilsa ONG institution. The tests performed showed that the system is capable of being used successfully by users of different ages and with different degrees of motor disability.

An artificial neural network multi-layer perceptron classifier was developed to make a diagnosis of Parkinson’s disease (PD) using a dataset obtained from the UCI Machine Learning Repository. The dataset consists of voice recordings from patients with PD and a control group. Multiple networks were trained to vary the number of neurons in the hidden layer between 10 and 6000 in steps of 10. The network with 280 neurons in the hidden layer had the best performance showing an accuracy of 95.23%, a precision of 96.40%, a recall of 97.10%, a specificity of 90.19%, and a F1-score of 96.75%. Artificial neural networks can be used to differentiate if a patient has PD using speech-related features. Furthermore, machine learning methods could predict other neurological diseases if the biomedical information is available.

The myoelectric control of upper limb prostheses from surface EMG signals is characterized by the ease of data collection and by being non-invasive. Multiple functions of a prosthesis can be activated from the signals generated by a voluntary muscle contraction. In this work, we present a system that activates four functions of a hand prosthesis with two EMG channels by means of a command strategy based on the maximum voluntary contraction (MVC). Experiments were performed on five healthy subjects and one amputee to evaluate the feasibility of this strategy. The results showed a fluid and intuitive strategy, justified by an accurate performance of the subjects controlling the prosthetic functions.

During a motor intent, desynchronization of sensorimotor rhythms of the cerebral cortex takes place. This physiological phenomenon, called event related desynchronization (ERD), is used for the development of Brain Computer Interfaces (BCI) for motor recovery. Stroke survivors suffering complete or partial loss of mobility usually require therapy for motor rehabilitation. The damage of the sensorimotor cortex that follows a stroke can compromise the brain rhythms that encode motor states. Therefore, this study assessed the ERD behavior during movement and motor intent in post-stroke patients. Eight electroencephalogram channels were recorded in seven chronic ischemic stroke patients during rest and movement or motor intent of their hands. The spectra of the coefficient of determination r2 were estimated between 8 and 30 Hz.The median of r2 reached 0.05 during motor intent of the corresponding affected hand; so it is a real value to be taken into account in BCI developments for people who have suffered a stroke. However, there was no evidence of a common pattern for the ERD. It was also observed that stroke survivors have discrimination capacity in the injured hemisphere during the motor intent of their affected limb.

A preliminary comparison was carried out among three novel strategies for automatic detection of ASSR versus the current alternatives implemented in AUDIX system. Two of the proposed strategies were faster in detecting the response and they did it robustly in the majority of the data set, even in subjects where current alternatives failed. This work provides evidence that improvements can still be done in the detection of ASSR by using appropriate statistical assumptions and averaging procedures.

In this work, we proposed the coupling of graphite pencil lead (GPL) electrodes with a custom designed 3D printed case and adjusting Velcro straps for the task of ECG acquisition. The proposed electrode and its attachment procedure were intended to provide reusable, easy-to-apply and long-lasting results in comparison to traditional adhesive electrodes, while simultaneously protecting the graphite-based electrode against mechanical stress. To this end, ECG data from twenty (N = 20) healthy volunteers were simultaneously acquired using commercial adhesive Ag/AgCl electrodes and the proposed cased and mechanically held graphite-based electrodes. The performance of the proposed electrodes was studied during still conditions via signal quality from ECG template signals as well as from heart rate (HR) time series. We found high correlation coefficients for the signal quality $$ (\rho = 0.9942) $$ and HR estimation $$ (\rho = 0.9495) $$ . The obtained results look promising and shown the feasibility of developing reusable, simple, low cost, and waste-free ECG electrodes alternatives to traditional disposable, adhesive ones.

Stress and illness have a complex relationship, that’s why stress is considered a precursor of illness, so it is important to study about it to reduce deceases and even prevent early death. In this work we employ two methods to measure stress: a subjective one known in the literature as Depress, Anxiety and Stress Scale (DASS) and an objective one proposed here based on the acquisition and analysis of certain bio-signals. The former is based on tests about how people feel, and the latter is based on bio-signal acquisition of temperature and galvanic skin response. In this paper, we use the Depress, Anxiety and Stress Scale method on its reduced version of 21 items as a reference to compare objective methods based on measurements of temperature and galvanic skin response of undergraduate students of High and Low Stress levels during three different conditions (i.e., closed eyes, open eyes and relaxation). We found that inter-individual variability of temperature and galvanic skin response does not allows us to show significant differences for subjects having low or high stress levels. In the other hand, for an intra-subject analysis differences among temperature or galvanic skin response shows significant differences between subjects having low or high stress levels.

Therapainting is a system based on head movements and eye blinks that allows the artistic expression for therapeutic purposes. Head movements and eyes blinks are respectively detected by accelerometers and electroencephalographic electrodes mounted on the headband MUSE developed by InteraXon from Toronto, Canada. On the basis of different exercises for people with spinal cord injuries, Therapainting makes use of head movements to move a paint brush across a screen, and eye blinks to select randomly different colors. Therapainting has been tested in non-controlled environments and by users of all ages at the Modern Art Museum, and at a high school of Autonomous University of Nuevo Leon in Monterrey, Mexico. User evaluation were carried out through the User Experience Questionnaire, which showed that Therapaining was considered as an interesting, exciting, innovative and leading edge system.

In Parkinson’s disease the symptoms of motor disability predominate, generating consequences in their quality of life, psychological, social and economic stability. The Unified Parkinson’s Disease Rating Scale is considered as a clinical evaluation method, that is a compendium of subjective interpretations. Alternatively a system called LEEPark is proposed, capable of quantifying the speed of the test of Alternating Hand Movement. The system uses Kinect V2 for data acquisition and features extraction, the results are stored in a database that is subjected to statistical analysis, grouped by degree of disease and by upper extremities. The analysis methods used were: ANOVA, linear regression and quantification of improvement, applied to data collected during 6 sessions from 6 patients: 4 with Parkinson’s disease and 2 control subjects. The results showed contrasts in hand speed, being able to find significant differences between grade 1 and 2 of the disease. It was also possible to establish the decrease in speed as the degree of disease progresses and the percentage of improvement when comparing the effects of medication on the speed of the hands.

Nowadays, in Latin America, a huge amount of people are in a motor disability condition. This phenomenon generates difficulties to execute daily tasks, such as the feeding process. To mitigate the daily difficulties, assistance devices are needed. This paper describes the evaluation of a brain-computer interface (BCI) for meal assistance, based on the sensorimotor rhythm (SMR), characteristic of the movement intention. The electroencephalographic (EEG) signals were acquired and processed to extract features in the frequency and time domain. These features train a classification model that separates the movement intention from any other cerebral activity. The study was made with ten healthy people who were subjected to a test that corresponds to feed themselves ten times. The results obtained show that average time to activate the meal assistance device is less than 10 s, furthermore, the accuracy of the tests performed was 81.6%, i.e. there is a good differentiation between a movement intention from another activity. Finally, it was concluded that the purposed meal assistance device achieves the goal of allowing an autonomous feeding and leaves as a precedent an alternative to implementing assistance devices through a BCI.

This work presents a low-cost solution for a neuro-rehabilitation system of stroke patients through a motorized pedal, as pedaling has the potential to provide a high number of flexion and extension repetitions of the lower limb. Stand-alone hardware and software were developed to control the motorized pedal, and the motor imagery recognition is done through a BCI, which analyzes EEG patterns on the motor region, related to feet movements and rest state. From this BCI, an avatar is triggered into an immersive virtual reality environment together the motorized pedal. sEMG signals collected on Rectus femoris (RF), Biceps femoris (BF), Tibalis Anterior (TA) and Gastrocnemius muscles (GM) allow identifying the muscle onset and offset when a force is exerted on the pedals, and force sensors are used to generate bio-feedback for the subject. As a first result of this setup, the maximum accuracy reached with our BCI was 94.41%.

The use of robotic lower-limb exoskeletons for rehabilitation of individuals with motor impairments is currently a tendency in several research groups, as they can be used to improve therapies in many applications in the field of physiotherapy. The main reasons are that they are safe, allow accurately controlling the range of movement of the individual’s lower-limbs, and can vary their speed and torque to make the movements, according to physiotherapist instructions. This work introduces the exoskeleton ALLOR (Advanced Lower Limb Orthoses for Rehabilitation), which was developed at UFES/Brazil, and presents an experimental protocol to be applied to rehabilitation of individuals with motor impairments. The proposed protocol is progressive and explores the different control strategies implemented in ALLOR: passive, mobilization, assisted, active and resisted, which are suitable for individual’s rehabilitation in three scenarios addressed in this work: stroke, total knee arthroplasty, and anterior cruciate ligament reconstruction. Clinical and functional parameters are also proposed to assess improvements in motion characteristics and its impact over general quality of life, locomotion and pain in the individuals.

People with severe motor dysfunctions need regular support to perform daily activities. Commercially are few affordable solutions that allow the control of everyday devices such as lamps, fans, and computers by tetraplegic. Thus, based on the concepts of assistive technology, this paper presents the development of a sip-and-puff interface that enables communication and control of devices. A hysteresis comparator circuit converts pressure signals into digital commands of sip-and-puff. The ARM Cortex - M4 STM32F429 microcontroller interprets the desirable actions and controls the graphical user interface on a VGA display. The system allows the activation of electrical equipment by relays and the operation of a computer through a virtual mouse and keyboard. Moreover, the invention handles a prototype of an adjustable bed, in which puff actions move DC motors that shift the structure. The results obtained with the prototype are satisfactory evaluating the ease of operation and its cost. These results encourage further studies with quadriplegic patients.

Patients with neurological injury rarely recover the functionality of upper limbs to the point of using it in an effective way in their daily life activities, such as personal hygiene and feed, becoming dependent of a third-party assistance. Thus, the objective of this study is to develop an assistive device to support the feeding of patients with motor impairment in upper limbs. Initially it was made the virtual prototyping of the device, split in a support base compounded by a bar which could be adjusted by the M6 screw coupled to a handle, three movable rods, and a cutlery base which the spoon is fixed allowing the feed of the individual. The device has four axis, the first one allows the device to rotate 360°, the second one adds freedom movement in the vertical axis, the third one allows movement in the horizontal axis so that the spoon could be directed to the patient’s mouth, and the last one keeps the spoon stable on the horizontal axis so that the food doesn’t fall. With this study, it was possible to develop a device capable of enabling patients with upper limbs motor impairment to feed themselves independently.

Body balance is an important basic sensory-motor skill. As technology advances, several forms of rehabilitation have been emerging. Thus, virtual reality games have increasingly been used in balance rehabilitation. This paper aims to present a literature review on the interventions performed with the use of balance games and their benefits for motor development. This theoretical study consists of a literature review. Original studies in English were included, as published in full in scientific journals, available in the following electronic databases: Bireme Virtual Health Library (VHL), PubMed, Medline and Cochrane. A total of 22 studies were found, out of which 8 articles were excluded, since they were duplicated, and another 2 ones, since they were directed to adults. Therefore, 12 articles were read in full. When reviewing the studies, it was possible to observe that rehabilitation with games in balance can be used for chronic non-evolving spastic encephalopathy, Down Syndrome or Developmental Deficit, as long as the child or adolescent has cognition and the possibility of staying in an orthostatic position, and Nintendo Wii™ and Microsoft Kinect™ can be used with the same objective. A higher number of studies using Nintendo Wii™ were observed in the literature. The review of the studies proved to be effective in the rehabilitation of balance with several pathologies. There has been much study on the use of games, not only to work balance, but also towards motor coordination and social development.

Recent investigations have proposed brain computer interfaces combined with functional electrical stimulation as a novel approach for upper limb motor recovery. These systems could detect motor intention movement as a power decrease of the sensorimotor rhythms in the electroencephalography signal, even in people with damaged brain cortex. However, these systems use a large number of electrodes and wired communication to be employed for gait rehabilitation. In this paper, the design and development of a wireless brain computer interface combined with functional electrical stimulation aimed at lower limb motor recovery is presented. The design requirements also account the dynamic of a rehabilitation therapy by allowing the therapist to adapt the system during the session. A preliminary evaluation of the system in a subject with right lower limb motor impairment due to multiple sclerosis was conducted and as a performance metric, the true positive rate was computed. The developed system evidenced a robust wireless communication and was able to detect lower limb motor intention. The mean of the performance metric was 75%. The results encouraged the possibility of testing the developed system in a gait rehabilitation clinical study.

This work presents the initial steps for the development of a serious game aimed at training users of upper limb prosthesis. In this initial phase, a virtual environment consisting of a soccer stadium was developed using Unity game engine. The game emulated the situation of a penalty kick where the users were able to control the direction to which the ball was kicked by contracting the muscles of the forearm. Experiments were performed on able-body subjects to assess their performance executing three series of ten penalties each. A score scale was implemented and the time needed to kick the penalties was measured. The results showed an intuitive and easy to play game, which was determined by the highly satisfactory performance of the subjects controlling the different functions of the game.

The essential tremor syndrome is a nerve disorder affecting around 77 million people worldwide. The causes are still not completely clear and there is no cure. Essential tremor is characterized by an uncontrollable “shaking” that affects greatly the quality of life of patients. The main treatments nowadays are drugs or surgery which have collateral effects, are expensive and does not fit to all the patients. Electrostimulation has emerged as an alternative treatment, however, the main drawback of this technique is the fatigue caused on the stimulated muscles as well as the difficult to make natural movements due to the stimulation. In this work is proposed a portable transcutaneous electrostimulation device based on a new type of stimulation that reduces the muscle fatigue by stimulating afferents pathways instead the muscle. The proposed device generates a bipolar current signal, 100 Hz pulse width of 30 μs and current from 0 to 20 mA. In this work is presented the methodology of design, preliminary results of the proposed circuit and the mechanical design of the portable device. The main contribution of this work is the design of a portable stimulator aiming to expand the research related to this technique.

The Paralympic boccia is a sport played by athletes with spasticity and severe impairment in the four limbs. Three (BC1, BC2, and BC3) of the four classes in boccia allow People with Cerebral Palsy (CP) to participate. The ranking is performed manually through the American Spasticity Scale. Like all qualitative scales, however, this evokes controversy among the evaluation board members who apply it, altering the classification of the athletes. A helpful option would be the use of mechanomyography (MMG), a method of acquiring muscle vibrations quantitatively based on accelerometers. Thus, the objective of this article was to conduct a pilot study analyzing the differences in the MMG signals of flexors and extensors muscles between two groups: 6 boccia athletes with spasticity and 6 non-impaired. Volunteers remained seated on the wheelchair, with their elbow bent close to 90°. They were verbally stimulated to perform the manual grip by pressing the evaluator’s hand while the MMG signals were recorded and stored. The mean MMGRMS descriptor was calculated for each signal. The experimental tests indicated differences between the two groups. For the spasticity group: 0.4058 and 0.4258 mV for the left flexor and extensor wrist muscles, respectively; 0.4215 and 0.4529 mV for the right flexor and extensor wrist muscles, respectively. For the non-impaired group, the MMGRMS was 0.3694 and 0.3945 mV for the left flexor and extensor wrist muscles, respectively; 0.35033 and 0.3728 mV for the right flexor and extensor wrist muscles, respectively. Thus, these results indicate greater spasticity for the spastic group and so, volunteers classification in lower levels.

Parkinson’s Disease is a neurodegenerative disease that affects fine movement control. It is generated by cellular death in the subtantia nigra which causes a decrement on the levels of dopamine in the body.Most treatments are based on the dosage of dopamine precursors or substitutes. These treatments alleviate the symptoms, such as the postural tremor, which is the most common in Parkinson’s disease. The downside to these treatments is, after approximately five years of usage, it turns ineffective, having to increment the dosage. It also has second-hand effects, such as vomit, loss of memory and dementia.Other treatments include movement therapy for bradykinesia and the use of orthosis for tremor suppression.The current orthosis has a constant opposition to the movement. This opposition generates fatigue and after time, muscular injury.To avoid muscular fatigue and reduce the tremor, an orthotic system with an adaptable friction coefficient was designed to diminish the tremor amplitude in upper limbs produced by Parkinson’s disease in the first and second stages. We developed a mathematical model to describe the arm’s biomechanical dynamics, simulated in MATLAB, and validated with a prototype and an oscillatory entrance. We applied a PI controller and tested different reduction systems in order to find a reduction method adequate and comfortable for a Parkinson’s Disease patient.

About half of the people with disabilities in Colombia manifest having impairments in the lower extremities, which requires them to perform any kind of physical rehabilitation, mainly balance recovery sessions. Often, these processes become tedious and monotonous for patients. Therefore, to address this issue, it is necessary to determine how emotional and psychophysiological responses are related to rehabilitation activities. We present a study performed by 13 healthy participants that includes the use of emotional self-reports and simultaneous psychophysiological measuring (15 features extracted) for two balance tasks, one based on regular rehab procedures and the other into a 3D virtual reality game. Results from this study shows that arousal (44%) and valence (24%) as well as heart rate (85%), skin conductance responses (40%) and respiratory rate (80%) outcomes tend to increase in the second task compared to first one. Though preliminary, these results indicate that rehabilitation process carried out through combination of VR environments, serious games and innovative technologies could improve balance recovery sessions, turning them into an enjoyable activity for participants.

The pupillary reflex reacts to the light dilating or contracting the pupil depending on different factors like the image, or the light. These changes on the size of the pupil let us to identify the amount of light that impacts into the eye. To correlate the light and the pupillary contraction it is necessary define the Retinal Illuminance, which the product of luminance, pupillary area and a constant that belongs to the dispersion of the light on the ocular media. This research is focused on the Retinal Illuminance in presence of color (RGB). For the study it was developed a stimulator capable of control the luminance in order to make it constant for each color. The study was made in a dark environment on healthy persons between 25 and 50 years old following the guidelines of Helsinki Declarations. It have been measured the maximum contraction of the pupil for each subject and each color. The results exhibited that Retinal Illuminance is color dependent and not only luminance dependent. The study was made on healthy subjects, this study can help us to establish which are the normal values of Retinal Illuminance to detect diseases that affect the color vision.

We constructed experiment system to verify the principle of CLOT using optical phantoms containing fluorophore. We used a digital mirror device (DMD) with high speed pattern switching capability (with interval of 1 ms). Fluorescent dye contained in a capillary with internal diameter of 1.4 mm was immersed in light scattering and absorbing medium contained intralipid. Location of the fluorophore was estimated by the proposed method. The reconstruction accuracy at the deep position (depth: 1.9 mm) was less than 0.4 mm. while the error was as large as 1 mm for the shallow position (depth: 0.6 mm) It is considered that accurate measurement of the optical constant is required for better reconstruction accuracy.

In this paper, an automatic method to process and quantify the metabolites of a magnetic resonance spectroscopy signal is presented. Signals obtained from a region of the left thalamus were analyzed. The obtained results were compared with the ones obtained using a Phillips’ commercial magnetic resonance software. Three features of the signal corresponding to five different metabolites (NAA, Cr, Cho, Glx, Cr2) were obtained: width, height and area of the curve corresponding to each peak. The obtained results showed a maximum relative error of over 20%, indicating the potential that the proposal has. Further refinements are required to reduce the error.

Nuclear magnetic resonance is a technique that produces high resolution volumetric images of the body without using ionizing radiation. The quality of images is significantly affected by electromagnetic fields in the environment, especially radio frequency signals and magnetic fields of 50/60 Hz, which results in the need for shielding of the room where the magnetic resonance imaging (MRI) equipment is installed. This study was undertaken to quantify the effects of poor shielding connections on image quality of MRI equipment. We compared the influence of poor connections on shielding effectiveness (SE) for magnetic fields of alternating currents in MRI cabinets with either ferromagnetic shielding (Fe-Si alloy non-oriented grain – NGO) or conductive shielding (Al). A computer simulation was compared to experimental measurements that used a metallic box with a volume of 1 m3 and gaps of 0.5 mm in the sheet’s connections. The SE of the Al shield was influenced more by the presence of gaps than the Fe-Si NGO shield.

Type 2 Diabetes Mellitus (DM2) is a disease that affects the following physiological systems: the central nervous system (CNS), the peripheral nervous system (PNS), the vascular system (VS), and the peripheral vascular system (PVS). When the PNS and PVS are affected, those complications are well known as diabetic neuropathy (DN) and diabetic vasculopathy (DV) respectively. These complications may cause lesions on the feet such as ulcers and are associated with other risk factors such as plantar pick pressure, friction from not wearing ergonomics footwear, the presence of biomechanics foot alterations, or significant temperature changes on the foot producing tissue infection that may cause lower limb amputation. The most important physiopathologies reported on the feet caused by the DN and DV are hypoesthesia and hyperthermia. These complications cause the loss of sensitivity and increase in temperature at the plantar surface respectively, and with continuously applied pressure, may initiate an ischemic or inflammatory problem. This multicausal condition of DM2 is known as Diabetic Foot Syndrome (DF), one of the main complications of the disease. Several studies have reported the correlation between ulcers and high temperatures under the plantar surface of the foot. Those temperature changes may be detected by using different methods. According to literary reviews, thermography is one of the methods most implemented by different researchers in laboratory environments. This method seems accurate in detecting temperature changes in the plantar foot anatomical regions by using image processing techniques, computer vision, and intelligent systems for improving ulcer detection in early stages of DF. The aim of this study is to define the benefits of using thermography as a future diagnostic tool for DF in a clinical environment based on the systematic literary review done in the last 15 years by BASPI-FootLab research group. Preliminary results of our review are reported in this paper.

Over the last year, bone tumor accounted leastwise than 1% of all new cancers diagnosis. The diagnosis is not usually opportune due to the few symptoms that patients present, and the options for treatment are generally invasive, like surgery or chemotherapy and radiotherapy, involved risk to the patient. In recent years, new therapies for treatment of bone tumors have being studied, such as high intensity focused ultrasound. This technology allows energy concentration at a specific point, named focus, which yields on a temperature increase in a short time. In this work, a parametric study of the acoustic propagation modeling in water of a spherical bowl transducer was evaluated in a frequency range from 1 to 10 MHz. The radiator geometry was established according to a commercial transducer with 4 MHz operating frequency. From the preliminary results, frequencies from 2 to 6 MHz were selected to analyze the acoustic propagation when cortical bone tissue with different thicknesses was added to the model. This study presented a shortening of focus for both different frequencies and bone thickness compared to the acoustic propagation of the transducer in water. The best results were frequencies from 3 to 5 MHz with bone thickness of 0.5 mm. Furthermore, the results gave an overview in the analysis of possible frequencies and bone thicknesses that could be used with more specific conditions to those described in this work.

Photothermal signatures of microcalcifications (MC) represented an important marker, that possibly later using the pulsed photoacoustic technique, this anomaly could be detected in early stages. Currently, MC are diagnosed by radiological images, however, their resolution is an impediment to detect smaller MC (<1 μm). For these reasons, this work presents pulsed photoacoustic technique as a possible method for microcalcifications early detection of and is also a non-invasive method. This method consists of irradiating the sample with near infrared light, and part of this light is absorbed by the microcalcifications, where these are heated causing a thermoelastic effect which generates mechanical waves that propagate at the sound speed and are detected by ultrasound sensors. The mechanical waves transmit information of the MC optical properties and when analyzing the amplitude, the delay time of the PA signal, and the vibrational modes of the frequency spectrum (FFT), it was possible to obtain the photothermal signature characteristic of microcalcifications and its depth in the breast tissue.

Impaired glucose tolerance (IGT), impaired fasting glucose (IFG) and insulin resistance (IR) are prediabetic conditions diagnosed by glucose and insulin values measured by oral glucose tolerance test (OGTT). In the OGTT, insulin and glucose levels are measured in five different blood samples: a sample in fasting (minute 0) and four others after oral intake of 75 gr of glucose, at intervals of 30 min (minute 30, 60, 90, and 120). The values of glucose at 0 and 120 min from OGTT are used for the diagnosis of IFG and IGT, respectively. The HOMA-IR is the most used method of determining IR in large population-based studies; it is mathematically derived from fasting glucose and insulin measurements from OGTT. One of the limitations of HOMA-IR is the difficulty of predicting IR in populations with IGT. The aim of this study is to evaluate the capability of a HOMA-IR and modified version of HOMA-IR (HOMA-IR calculated from glucose and insulin of 30, 60, 90 and 120 min of OGTT) to diagnose IGT, IR, and IFG. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were performed to assess the predictive capacity of HOMA-IR and modified versions. The present study demonstrated that modified versions of HOMA-IR calculated using glucose and insulin from 60 and 90 min of OGTT are alternative indexes (AUC ≥ 0.70) for IGT, IFG and IR detection.

Hospitals and health centers that own a pharmacy area require continuous and safe monitoring of environmental variables. The rigorous control of temperature and relative humidity allows the physical and chemical properties of the medicines to be conserved, according to the suggestion of suppliers and current laws that regulate the conservation of medicines. The Internet of things (IoT) allows for monitoring and controlling specific areas by obtaining and publishing relevant data directly in the cloud, enabling intelligent systems to make early decisions when an abnormality is present in the area. However, for the IoT to fulfill its purpose, there must exist a permanent connection of things to the Internet, which is a challenge when there is low or no connection. Therefore, the purpose of this work is to implement a monitoring system for environmental variables under the condition of low or no Internet connectivity, using alternative technologies such as (GSM) for transferring data to the cloud. As a result of this work, the design and implementation of a continuous monitoring system for environmental variables in the hospital pharmacy area is presented. This system guarantees the integrity and security of the acquired data due to the standardized communication protocols that were implemented and the alternative IoT architecture under which the system was designed.

By using new technologies in electronic games, virtual environments come up with the proposal of simulating daily routines. This research has intended to design a virtual game focused on performing palmar grip, cylindrical grip and tip-to-tip pinch movements. A virtual game was designed in three different stages, the first of which stage aiming to stimulate the palmar grip movement, the second one stage to stimulate the tip-to-tip pinch movement, while the third stage focused on furthering the cylindrical grip movement. The game features a timer, which determines three minutes for each stage, while the player’s performance is recorded in order to measure his or her progress in performing each movement. In order to activate the virtual game, it is required using a device to capture hand movements, named Leap Motion controller. By doing that, it is possible to interact with and move characters in the game. During software engineering tests, no operational problems were found in the system. The virtual game is able to stimulate in a playful manner the grip and pinch movements and can be a tool used for training such movements.

This paper presents a methodology proposal for defining indicators in ubiquitous management on primary health care. With a multidisciplinary team, health technology indicators were identified through quality tools to classify fault parameters. Data collection was performed in a clinical engineering information system, with equipment that compose the technologic park of primary health care from 2014 to 2018. In the categorization of the data, sixty classes of equipment were listed, totaling 3053 equipment distributed in the network of primary care in the city of Florianópolis-Santa Catarina-Brazil. In these five years, 17638 work order accounted for the study were generated. After applying quality tools, such as Ishikawa diagrams, the dental compressor was chosen to validate the proposal and determine the parameters to be monitored in order to support the performance of clinical engineering in a predictive model. This methodology enables maintenance management, providing reliability and safety to the primary care system.

This paper presents a modular system of wireless monitoring of physiological and ambient conditions based on Internet of Things (IoT). It is specially designed for elderly people and people with chronic diseases to promote their independent living. The system consists of a network of physiological and home ambient sensors developed under standard IoT protocols. These sensors are designed and manufactured at the laboratory with low-cost components. In addition, commercial sensors can be added according to the requirements of the patient. The system also provides a web based user interface for medical and family caregivers. It displays real time data and it includes an alarm system to report unsafe events via email. The caregiver can also see historical data with the possibility to compare all the parameters of a certain day and time. Furthermore, a home automation system was proposed to control home devices to improve home safety. The evaluation of the system was performed under a controlled environment at the laboratory in pilot tests before being tested in patients.

Diabetes represents an ever-increasing risk for the world population, and the rise of its prevalence is not expected to cease. To combat this alarming problem is the concomitant rise of new and improving mobile phones and wearable devices that can be exploited to fight diabetes. This paper presents a prototype of a monitoring system that will serve as a tool for improved patient monitoring by health care professionals (HCPs). Several physiological measurements were selected to perform the monitoring so that important data about a patient’s status can be collected and processed. Mobile technologies, cloud services and wireless protocols were used to develop a mobile application that can handle several devices and the data they gather. The resulting implementation provides a powerful infrastructure that could supply feedback to HCPs and patients as meaningful data, which can then be processed and potentially extend the assessment capabilities of an entire health care system.

Internet of things technology has grown in recent years. One of the sectors where it has had the greatest reception is sports. In Colombia, for every 61 high-performance athletes there is, on average, one coach. For this reason, the purpose of this work was to validate an intelligent device that enables remote monitoring of the speed of high-performance roller skaters using the Internet of things. The authors equipped professional Luigino skates with electronic instrumentation, with which it was possible to measure a high-performance skater’s linear speed and distance traveled of by means of Hall effect sensors and a magnet. To eliminate false measurements, a distance sensor was also installed in the skate skids to determine when the skate was in contact with the ground. This information was sent to the Internet through the MQTT protocol to a main broker that was responsible for the processing and visualization of the data. The linear velocity was validated at four speeds (0.8, 5, 8 and 10 km/h) using a treadmill. The error of the measurements was between 1% and 5%.

The simultaneity between the current information and communication technologies (ICT), which with the current industrial revolution classified as 4.0, aims that all the everyday objects with which a person interacts allow to provide relevant information of a specific environment through Internet. The field of medicine and more specifically, medical devices allow us to contemplate a shared vision of interconnected sensors that can improve medical care services, with the possibility of the appearance of new technologies such as the Internet of Things (IoT) promise to be the field to follow in the near future. This paper offers an approach to the implementation of a real-time monitoring system on the main variables of a Computerized Axial Tomography (CAT) in the imaging service of a fourth level hospital through the IoT. The information obtained about the different sensors implemented in the medical team, suggested relevant information about the importance of monitoring the main variables that are directly related to the operating specifications suggested by the manufacturer of the medical equipment. The data collected were analyzed using the “regression” diagnostic test as a probabilistic strategy to look for a predictive value that suggests a possible failure of the medical team. The results showed that the information collected suggests the importance of monitoring the operating parameters of the CAT continuously given the behavior of some atypical data that were obtained, although there was no failure in the time window where the CAT was performed.

The most frequent and troubling side effect in the treatment of hemodialysis is intradialytic hypotension (IDH). This compromises cardiovascular hemodynamics. Heart rate variability (HRV) is a non-invasive measurement of the sympathovagal balance, and provides important information about the autonomic nervous system. Few studies have compared hypotension in diabetic and non-diabetic patients throughout the therapy sessions, with a protocol of intradialytic exercise and high flux hemodialysis/hemodiafiltration therapy. Our work is aimed to evaluate cardiac autonomic regulation during hemodialysis with HRV in hemodynamically unstable patients with chronic renal disease. During a 6-month follow-up of patients, the model suggested that the sympathovagal index (LF/HF) is different between the diabetic from non-diabetic group (p < 0.05), and does not necessarily reflect the sympathetic balance. Diabetic patients were differentiated by having lower HRV power components than the non-diabetic group (p < 0.05), and during the hypotensive event they showed a sympathetic inhibition, unlike the non-diabetic group that showed a parasympathetic inhibition. It was observed that the compensatory mechanisms were markedly different between two groups studied. This study shows the importance of giving individualized attention and therapies in order to take preventive measures to avoid hypotensive events.

Human Immunodeficiency Virus (HIV) damages the immune system and AIDS (Acquired Immune Deficiency Syndrome) occurs in the final stage of the virus when it is not treated. This situation keeps being a global health problem, and efforts have focused on research for a cure. In general, the diagnosis focuses on clinical blood examinations of the lymphocyte cells behavior and the treatment with medications and vaccines. Due to the complexity of the disease, interventions that involve different aspects of the diagnosis and treatment are, in course. This paper presents a prioritization system for timely help the healthcare of HIV/AIDS patient. It relies on a model built using multiple criteria decision analysis that employs variables related to information of the patient. The model output is an index that maps into an appropriate care scale that leads to managing timely actions, aims to optimize the treatment, and supports the patient to take control of the disease. We validated the model with simulated data, obtaining a 94.8% of correct class assignment using a decision tree. We also tested the model with case studies data with 80% of correct assignation. The results show that the prioritization assignment tends to be founded on the variables patient profile and time of disease, although more research on the variable analysis and data collection is mandatory.

Health care requires the development of activities devoted to providing the health services that people needs and demands. Within these activities, the clinical procedures, as well as the use of technological resources, have had a predominant role in the accomplishment of these goals. That is why effective management of health technologies is a strategic objective that has been globally disseminated into health programs worldwide. The incorporation of stakeholders in management activities, particularly in the evaluation of medical technologies, has proven to be more complicated than it seems because of both the multidisciplinary nature of the functions and the interests and motivations that these activities promote. Given the important role that the stakeholders play in the health technology assessment (HTA) activities, an approach supported on administrative sciences and organizational theory to analyze and manage their participation is presented. Three principal issues are addressed: the identification of the stakeholders, the analysis of their dynamics and the development of strategies for their management; then some tools to deal with them are shown. A general example centered on the biomedical engineer that performs an analysis of a medical device for purchase is exposed.

The technical status of the medical equipment is a criterion used to define its replacement. In this paper we present two indicators to evaluate the technical status of the medical equipment developed in two National Health Institutes from Mexico. The objective was to do a comparative analysis between both indicators based on the evaluation of a sample of 28 medical equipment located in critical care areas of one of the Institutes. The results showed that there is a direct relationship between both indicators and the technical status is a direct criterion for providing useful information to plan the replacement of medical equipment.

The objective of this work was to develop an indicator to evaluate the availability of medical equipment located in four critical care areas of the Mexican Institute of Pediatrics. The indicator considers the demand and supply of medical equipment, and estimates its use from patients seen in the corresponding medical service. Given that we want to know if the medical equipment installed in the critical units is sufficient for patient care, the interpretation of the indicator result was made through two economic concepts: deficit and surplus. The indicator was applied to a sample of 38 medical equipment of seven type, and their results showed which equipment is sufficient and which has a deficit. This information is useful for the medical personnel and technical staff of the Electro-medicine Coordination of the Institute, in the identification of opportunity areas that allow improving the care of patients attended by the Institute.

Clinical engineering (CE), also known as healthcare technology management (HTM), professionals in many countries have experienced difficulties in obtaining recognition and support from their senior leadership, despite the fact that they have been successful in maintaining and managing healthcare equipment in safe and reliable condition. A fundamental root cause of this paradox is the lack of metrics to measure and compare CE performance, as equipment safety and reliability are not easily measured, much less the impact of equipment maintenance and management on the health outcomes of patients. CE professionals have been reluctant to measure and compare performance, believing such exercises require sophisticated skills and may expose them to closer scrutiny by their supervisors or lead them to become targets of outsourcing companies. Studies conducted with data from hundreds of American acute care hospitals have proven that these concerns are unfounded; only some readily available datasets are needed and analyses can be performed with standard spreadsheets. Well-performed benchmarking is helpful not only for CE managers to earn better recognition and support from their supervisors, but also to reduce the risk of having their team replaced by outsourcing companies. This paper provides a practical guide for CE benchmarking.

The present work describes the experience that involves the valuation of medical equipment for the establishment of the first and third breast cancer clinic in Mexican Social Security Institute (IMSS), CDMX South Delegation, as part to develop national detection, diagnosis and treatment breast cancer programmed with optimal quality, friendliness and respect for patients’ rights.In this case, the main arguments presented refer to the guidelines used to validate the performance of digital mammography, since this team is representative of this project, for which templates of technical information were developed and applied to six mammography equipment’s, the concepts to intervene in the analysis of the technical specifications, includes the response of detectors, dosimeters, contrast and image quality among the main aspects.It is important to mention the efficiently that the medical units offer quality medical attention, it required their equipment works efficient to guarantee their availability and the safety of both patient operators of the equipment.With the premise of sharing, the experiences obtained in the creation of these clinics, and the roll of the biomedical engineer as part multidisciplinary team, is considered the responsible in optimization medical technology management.

In the present work, we describe the design and implementation of a Web information system for the management of technology and medical infrastructure for a medical center, with proper capacities to be used at the governmental level. The work begins with analysis of the workflow that a Clinical Engineering Department follows during corrective or preventive operations on the infrastructure and medical technology in order to keep the electromedical equipment and the infrastructure in optimal state of operation. For the design, development and implementation of the IT solution, the freely available tools LayoutIt, Bootstrap framework and XAMP Web server were used. The system uses the PHP language combined with HTML to provide a user-friendly interface that allows following step by step the flow defined for the management of the interventions, in addition to sending emails and generating work material in PDF format. At the same time, it records the necessary data to be used later by the statistics module. This Web system is published and in use by the professionals of the Clinical Engineering Department of the Hospital de Córdoba in the province of Córdoba, Argentina.

Healthcare managers are interested in identifying decision-making tools that consider multiple criteria to support the acquisition of new medical equipment. Multiple Criteria Decision Analysis (MCDA) is increasingly used for the Health Technology Assessment (HTA). This paper presents a model for the prioritization of alternatives for POCT blood gas analyzers in a healthcare institution in the city of Cali, Colombia using the Analytic Hierarchy Process (AHP). This work relates the different opinions of the main actors of the equipment acquisition process with scientific and technical elements. The result of the study is a model for the evaluation of medical equipment tailored to the needs of the hospital. This implementation is a progression towards the integration of scientific evidence and decision-making in healthcare institutions.

Energy expenditure has been proposed for the evaluation of rehabilitation treatments. Traditionally it has been estimated by oxygen uptake and the use of spirometry. However, this method is not often appropriate for the clinical environment given that the equipment is cumbersome and may affect the results of the measurements by causing discomfort to the person. Indices based on heart rate have been proposed as alternatives. One of those indices is the Total Heart Beat Index (THBI). The purpose of this investigation was to evaluate a protocol to measure THBI in a research environment and in a clinical environment. For this, measurements were performed in ten young adults in a training area (research environment) and three older adults in a clinical environment. Heart rate was measured continuously while participants walked in a 8 shape walkway. The results showed results similar to those reported in the literature, with intraclass correlation coefficients (ICC) larger than 0.8. The protocol was feasible to be used in both environments and it was a simple to use, relatively low cost and portable tool for estimating energy expenditure.

Nasal injury from the use of prongs in non-invasive mechanical ventilation (NIV) is a very common adverse event at Neonatal Intensive Care Units (NICUs). The goal of this research was to evaluate the feasibility of reuse of sterilized binaural prongs in newborns (NBs) submitted to NIV. In order to reach this goal, we have compared eight NBs were submitted to NIV using sterilized prongs (SP Group – SPG) and 11 others with brand-new prongs (NP Group – NPG). The frequency and severity of external nasal injury were evaluated daily through clinical inspection. The frequency of external nasal injury was 87.5% for SPG, and 90.9% for NPG (p = 1.00). Of the 7 external nasal injuries found in the SPG, 4 (57.14%) were Stage I, and 3 (42.86%) were Stage II. Of the 10 external nasal injuries found in the NPG, 9 (90%) were Stage I, and only 1 (10%) reached Stage II. Although the results of this study did not show a significant statistic difference, the use of sterilized prongs did increase the severity of the external nasal injury, making its use contraindicated in newborns undergoing NIV.

Conventional health practices have been more costly to the health. Remote monitoring in the prevention and control of chronic noncommunicable diseases is a way to reduce costs. Thus, the main motivation of this study was to analyze the economic results of the use of the Remote Monitoring System in obese children. The objective was to evaluate the economic impact of remote monitoring of obesity, and it is based on direct costs compared to the conventional health care system. The methodology was based on the accounting data, in which the direct costs of the care and procedures of health professionals were calculated. The results obtained from the costs of caring for 150 children were the following: costs with remote monitoring of US $ 7,689.00 and conventional health practices of US $ 11,169.00. It is concluded that remote monitoring presented cost reduction and generated savings. In the long run it can present social and economic results for health, and to the government.

Research in Primary Health Care (PHC) has advanced in recent years, mainly with preventive and curative strategies or actions in family and community orientation through multi-professional teams. In health systems that value equity, integrality and social participation, these professionals perform activities based on reliable, structured and current data. Therefore, the use of permanent education together with distance education is the key to training these professionals, especially with virtual learning environments. However, few papers discuss, present and catalog the results already obtained in the community. Thus, the objective was to locate in the literature the aspects of virtual learning environments in teams focused on primary health care. Thus, a search for articles in English was made in the databases PubMed Central®, IEEE Xplore®, CAPES Periodical®, and Elsevier’s Scopus®. The results of the analyses provided an overview of Virtual Learning Environments. In the meantime, it has been found that many are unavailable or have access restrictions, which makes it impossible to reuse them.

Monitoring electrical failures in biomedical devices makes it possible to ensure patient’s safety. While assessing risk in medical equipment, we observed that electrical failures are one of the adverse events detected. Biomedical laboratories include procedures and design devices for testing electrical safety. In this work, we develop procedures to perform electrical safety test based on national and international standards. One of them is the Protective Earth Resistance test. Currently, the main standard applied to medical equipment tests is IEC 62353: 2014 “Medical electrical Equipment - recurrent test and test after repair of medical electrical equipment”. Specifically, this standard is a guideline that describes the requirements to ensure the electrical safety through comparable and reproducible tests. To ensure technical competence, laboratories must validate their testing procedures following the standard ISO/IEC 17025: 2017 “General requirements for the competence of testing and calibration laboratories”. This standard comprises all the requirements that laboratories must achieve to demonstrate that they (i) apply a quality management system, (ii) are technically competent and (iii) they may obtain valid technical results.

Clinical Engineering applies methodologies that guide maintenance management systems so they can comply with regulations and standards. In many healthcare institutions, these guidelines are difficult to follow strictly due to different factors. The objective of this study was to define the requirements that a maintenance management system must complete, following national regulations and international accreditation standards. To achieve this goal, we conducted a web search to identify the standards associated with national and international medical devices. The World Health Organization was taken as a benchmark to compare them. In our search, we identified significant similarities, concluding that regulations in Colombia follow international standards, but there is still a long way to go, and studies like this are vital in order to achieve it.

This work presents a fault prioritization system based in two MISO (Multi Input Single Output) fuzzy logic systems, applied on a vital signs monitor. The MISO systems correspond to possible failures, classified as physiological and technical failure. For physiological failures, the fuzzy inputs are heart rate, respiratory rate, temperature and the partial oxygen saturation variables, the membership functions for each variable were designed using the normal range for a healthy adult. Technical failures system considered faults in the monitor’s arrange of sensors as inputs, while it’s membership functions vary accordingly to the operational status of the sensors. Both fuzzy systems have as output four priority levels, whose labels are: normal, mild, moderate and critical. Once the design of the MISO systems was carried out, three trials were performed on the fault prioritization system: a Monte Carlo test for both subsystems, simulated patient test for physiological failures and real patient with sensors connectivity alterations for technical failures. Due to the results achieved, the fault prioritization system can be applied in the automation of alarms or can be a reference for the development of future Fuzzy systems applied to different types of vital signs monitors.

This work will analyze and evaluate various algorithms to carry out a preventive maintenance program for medical equipment at the ISSEMyM Toluca Medical Center “Lic. Arturo Montiel Rojas (CMIT) that is part of the hospital services of the Institute of Social Security of the State of Mexico and Municipalities (ISSEMyM).

Traditional evaluations of knowledge in engineering education usually focus on the assessments gathered by the teacher from different sources such as exams, essays and laboratory reports. However, this is a single actor’s point of view of a learning process: the teacher. The self-evaluation and feedback provided by classmates may also represent an important and useful contribution in at student’s learning process. This paper presents an integrated evaluation strategy that includes four equally important components: teacher evaluation, self-evaluation, peer evaluation and group work evaluation. A pilot study was carried out with 20 students taken the Biophysics 1 course offered by the Universidad del Rosario’s biomedical engineering program. Results reveal different performances within the groups, allowing the detection of cases where students showed difficulties. Furthermore, it demonstrates the importance of self-criticism and how to positively influence the learning process of others.

Pressure sensing by measuring the impedance of soft matter, like hydrogels and rubber, has so far posed the problems of low conductivity and hysteresis. Further modifications in the materials to enhance their properties are needed. A pressure sensor using a simple gelatin/agarose double network gel placed between two Ag/AgCl electrodes is presented. The impedance spectra from 100 Hz to 1 MHz were investigated and admittance calculated. Both response time and hysteresis were also investigated. All the experiments were made using an impedance analyzer from Zurich Instruments. Results showed that the lowest standard deviation is at 3.5 kHz. It was found that the admittance increases exponentially from 7.9 to approximately 11.7 mS with increasing the mechanical load at 3.5 kHz. The highest hysteresis of about ±7.6% was found to be at zero load. The measured response time of the gel impedance was approximately 0.5 s, which makes the double network gel feasible for real time pressure monitoring. Mechanical tests measuring the force on the gel due to deformation showed little influence of the gel structure on the impedance. Thus it was concluded that the change of the distance in between the electrodes is responsible for the impedance change of the gel. Calibrations will be necessary for practical applications, since the gel impedance changes as environmental conditions change. This sensor showed better results as compared to conductive rubber published in the literature. It can be concluded that agarose/gelatin gels provide a cheap and easy to fabricate pressure measuring device.

The development of biosensors to identify the molecular markers of specific genes is fundamental for the implementation of new techniques that allow the detection of specific DNA sequences in a fast, economic, and simple way. Electrical Bioimpedance Spectroscopy (EBiS) has been used for the diagnosis and monitoring of human pathologies, and is recognized as a safe, fast, reusable, easy, and inexpensive technique. This study proves the development of a complementary DNA (cDNA) biosensor based on measurements of EBiS and of the immobilization of DNA without chemical modifications, and presents the evaluation of its potential usefulness in the detection of the gene expression of an inflammation characteristic biomarker, NLRP3, is presented. The obtained results demonstrate that EBiS can be used to identify different gene expression patterns, and measurements were compared with Quartz Crystal Microbalance with Dissipation monitoring (QCM-D) and validated by Quantitative Polymerase Chain Reaction (qPCR). These results indicate the technical feasibility of a biosensor of specific genes through bioimpedance measurements in the immobilization of cDNA.

This paper presents the control of a lower-limb exoskeleton in real time, to assist a pathological gait, taking into account only electromyography (EMG) events and a characteristic angular pattern of a normal gait. The algorithm consists of an impedance control scheme that uses EMG signals to modify the characteristic angular pattern of a normal gait. EMG signals were characterized using the simple square integral (SSI) index and this information was used to identify a characteristic EMG pattern in the semitendinosus and gastrocnemius muscles. Finally, a series of experimental tests were conducted with a volunteer to validate the adequate performance of the system.

In this work, the development of a continuous monitoring system of a human’s respiratory rate (RR) is presented. The system is based on the impedance pneumography method and it is divided into two main blocks: the electronic circuit and the electrodes. For the electrodes, two technologies are considered: the first one is Ag/AgCl disposable electrodes frequently used in clinical applications. The second one, known as textile electrode, is built on electrically conductive fabric. The performance of both types of electrodes is compared. Therefore, the experimental tests are carried out in two stages. In both stages, 5 records of 10 min were acquired in 5 healthy subjects. The first stage is recorded with disposable electrodes and the second one with textile electrodes. In order to compare the results obtained in each case, reference values were simultaneously recorded in each subject with a commercial polysomnography system. This system records respiratory activity using a method based on piezoelectric respiratory effort bands placed in the abdomen and thorax. Finally, the absolute (eA) and relative (eR) errors are computed taking into account RR values obtained from the designed and reference system for each stage. The results show similar performance among textile and disposable electrodes.