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2015 | Buch

How to Cross the Border from R to D? The Example of Conception of New Medical Devices Kapitel lesen Erstes Kapitel lesen

Biomedical Engineering Systems and Technologies

8th International Joint Conference, BIOSTEC 2015, Lisbon, Portugal, January 12-15, 2015, Revised Selected Papers

herausgegeben von: Ana Fred, Hugo Gamboa, Dirk Elias

Verlag: Springer International Publishing

Buchreihe : Communications in Computer and Information Science

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This book constitutes the thoroughly refereed post-conference proceedings of the 8th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2015, held in Lisbon, Portugal, in January 2015.

The 27 revised full papers presented together with an invited paper were carefully reviewed and selected from a total of 375 submissions. The papers cover a wide range of topics and are organized in four general topical sections on biomedical electronics and devices; bioimaging; bioinformatics models, methods and algorithms; bio-inspired systems and signal processing; health informatics.

Inhaltsverzeichnis

Frontmatter

Invited Paper

Frontmatter
How to Cross the Border from R to D? The Example of Conception of New Medical Devices
Abstract
The border between Research and Development for a new medical device is often unclear due to non-linear process of its development and frequent feedbacks from trials in clinical settings to a new conception of the product. Sometimes researchers under-estimate these translational studies since they do not lead to an increase of fundamental knowledge. However, and especially in the field of medical devices, users have to face specific difficulties due to the variability of the biological systems under study. Results obtained in translational research often depend on this variability and new questions or scientific obstacles arise from the confrontation to the real world. In order to address these new challenges, reverse translational research is required. Fundamental research is then fuelled by the results of translational research. A useful model of medical device development is presented here through several examples of translational research.
Lionel Pazart

Biomedical Electronics and Devices

Frontmatter
Low Power Programmable Gain Analog to Digital Converter for Integrated Neural Implant Front End
Abstract
Integrated neural implants interface with the brain using biocompatible electrodes to provide high yield cell recordings, large channel counts and access to spike data and/or field potentials with high signal-to-noise ratio. By increasing the number of recording electrodes, spatially broad analysis can be performed that can provide insights on how and why neuronal ensembles synchronize their activity. However, the maximum number of channels is constrained by noise, area, bandwidth, power, thermal dissipation and the scalability and expandability of the recording system. In this chapter, we characterize the noise fluctuations on a circuit-architecture level for efficient hardware implementation of programmable gain analog to digital converter for neural signal-processing. This approach provides key insight required to address signal-to-noise ratio, response time, and linearity of the physical electronic interface. The proposed methodology is evaluated on a prototype converter designed in standard single poly, six metal 90-nm CMOS process.
Amir Zjajo, Carlo Galuzzi, Rene van Leuken
Integrated Chip Power Receiver for Wireless Bio-implantable Devices
Abstract
Wireless bio-medical devices employ inductive link as medium for transfer of energy between the external source and the implant. But, the inductive power picked by receiver results in high voltage, that may largely exceed the voltage compliance of low voltage integrated chips. The high voltage at the receiver is due to high load impedance offered by electrodes within the implant. To limit the magnitude of induced voltage, majority of the low voltage circuits use power inefficient methods like voltage clippers and shunt regulators. Therefore, to overcome voltage limitation and to enhance power efficiency, a power receiver topology based on step-down approach is designed and implemented for input voltage as high as 30 V. The implemented design consists of rectifier and series voltage regulator. In addition a battery charger circuit that ensures safe and reliable charging of the implant battery is designed and tested. The proposed design is fabricated in 0.35 \(\mu \)m high voltage BCD foundry. Rectifier and regulator power efficacy are analyzed based on simulation and measurement results.
Vijith Vijayakumaran Nair, Jun Rim Choi
Research on a Novel Three-Channel Self-pressurized Wrist Pulse Acquisition System
Abstract
This paper proposed a novel three-channel self-pressurized wrist pulse acquisition system based on the principle of wrist pulse diagnosis in Traditional Chinese Medicine (TCM). This proposed acquisition system could not only sample, display, analyze and store the wrist pulses in Cun, Guan and Chi region of the wrist simultaneously under the best pulse taking force, but also simulate the process of wrist pulse diagnosis in TCM clinical science called “three regions and nine pulse takings”. It is believed that this proposed system solved the problem that most of the current pulse acquisition systems could only detect the pulse in Guan region of the wrist. The result shows that the proposed acquisition system provided a user-friendly flexible sample collection platform, laid the foundation for further analysis of multi-channel wrist pulses and pushed forward the development of the standardization of wrist pulse waveforms and the objectification of wrist pulse diagnosis in TCM.
Zhou Kan-heng, Qian Peng, Xia Chun-ming, Wang Yi-qin
A Wearable Device for High-Frequency EEG Signal Recording
Abstract
The recording of high-frequency oscillations (HFO) through the skull has been investigated in the last years highlighting interesting new correlations between the EEG signals and common mental diseases. Therefore, since most of the commercially available EEG acquisition systems are focused on the low frequency signals, a wide-band EEG recorder is here presented. The proposed system is designed for those applications in which a wearable and user-friendly device is required. Using a standard Bluetooth (BT) module to transfers the acquired signals to a remote back-end, it can be easily interfaced with the nowadays widely spread smartphones or tablets by means of a mobile-based application. A Component Off-The-Shelf (COTS) device was designed on a \(19\,\text {cm}^{2}\) custom PCB with a low-power 8-channel acquisition module and a \(24-bit\) Analog to Digital Converter (ADC). The presented system, validated through in-vivo experiments, allows EEG signals recording at different sample rates, with a maximum bandwidth of \(524\,\text {Hz}\), and exhibits a maximum power consumption of 270 mW.
Lorenzo Bisoni, Enzo Mastinu, Massimo Barbaro
Indirect Blood Pressure Evaluation by Means of Genetic Programming
Abstract
This paper relies on the hypothesis of the existence of a nonlinear relationship between Electrocardiography (ECG) and Heart Related Variability (HRV) parameters, plethysmography (PPG), and blood pressure (BP) values. This hypothesis implies that, rather than continuously measuring the patient’s BP, both their systolic and diastolic BP values can be indirectly measured as follows: a wearable wireless PPG sensor is applied to a patient’s finger, an ECG sensor to their chest, HRV parameter values are computed, and regression is performed on the achieved values of these parameters. Genetic Programming (GP) is a Computational Intelligence paradigm that can at the same time automatically evolve the structure of a mathematical model and select from among a wide parameter set the most important parameters contained in the model. Consequently, it can carry out very well the task of regression. The scientific literature of this field reveals that nobody has ever used GP aiming at relating parameters derived from HRV analysis and PPG to BP values. Therefore, in this paper we have carried out preliminary experiments on the use of GP in facing this regression task. GP has been able to find a mathematical model expressing a nonlinear relationship between heart activity, and thus ECG and HRV parameters, PPG and BP values. The experimental results reveal that the approximation error involved by the use of this method is lower than 2 mmHg for both systolic and diastolic BP values.
Giovanna Sannino, Ivanoe De Falco, Giuseppe De Pietro
Non-invasive Wireless Bio Sensing
Abstract
Wireless sensing technologies are increasingly being employed on Health systems, aiming to improve the data communication flow between patients and clinical experts. This is especially important for patients located at remote locations or facing mobility constraints. In order to fully exploit the advantages of wireless communications, it is necessary biosensors that collect data about user’s health, possibly integrated on a personal wireless sensor network. With this goal in mind, a wireless solution is described that presents an innovative wireless heart rate device, as well as user interface technologies for enabling real-time data visualization on mobile devices by patients and medical experts.
Artur Arsenio, João Andrade, Andreia Duarte

Bioimaging

Frontmatter
Crutchfield Information Metric: A Valid Tool for Quality Control of Multiparametric MRI Data?
Abstract
We propose an information theoretic framework to automatically infer the physical relationship and asses the quality of multiparametric MRI sequences. The method is based on the Crutchfield information metric. This distance measure can be computed solely based on the voxel intensities. In a series of experiments we proof its usefulness. First, we show that given multiparametric MRI data sets it is possible to discover the physical relationship w.r.t. the acquisition parameters of the individual sequences. Next, we demonstrate that this relationship can be employed to perform a quality check of a large (\(N=216\)) data set by identifying faulty components, e.g. due to motion artifacts. Finally, we use a multidirectional diffusion weighted data set to confirm that the approach is fine grained enough to even detect small differences of diffusion vectors as well as the direction of the phase encoding of an echo planar imaging (EPI) sequence. Future work aims at transferring the preliminary results of these promising experiments into clinical routine and at standardizing MRI protocols for large scale clinical trials.
Jens Kleesiek, Armin Biller, Andreas J. Bartsch, Kai Ueltzhöffer
Medical Image Retrieval for Alzheimer’s Disease Using Structural MRI Measures
Abstract
The aim of the paper is to study medical image retrieval for Alzheimer’s Disease (AD) on the bases of structural MRI measures. The main goal of the strategy used in this paper is to improve the retrieval performance while using smaller number of features. The feature vector consists of the measurements of cortical and subcortical brain structures, including volumes of the brain structures and cortical thickness. The feature subset selection is additionally applied using the Correlation-based Feature Selection method to exclude irrelevant, redundant or possibly noisy data and to consider the most relevant and discriminative features. Six different scenarios for the image representation are studied: volumetric features, cortical thickness features, all imaging features, selected volumetric features, selected cortical thickness feature and selected imaging features. Euclidean distance is used as a similarity measurement. The dataset used for evaluation of the retrieval performance is provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Experimental results show that the strategy used in this research outperforms the traditional one despite its simplicity and small number of features used for representation. Additionally, the performed analysis demonstrated that the selected features are highly stable through the leave-one-out strategy. Moreover, they are stressed in the literature as significant biomarkers for Alzheimer’s Disease, which makes the strategy used in this research even more reasonable.
Katarina Trojacanec, Ivan Kitanovski, Ivica Dimitrovski, Suzana Loshkovska, for the Alzheimer’s Disease Neuroimaging Initiative*
An Iterative Mesh Optimization Method for 3D Meristem Reconstruction at Cell Level
Abstract
This paper focuses on the reconstruction of 3-dimensional multi-lay-ered triangular mesh representations of plant cell tissues, based on segmented images obtained from confocal microscopy of shoot apical meristems of model plant Arabidopsis thaliana. Obtaining good-quality meshes of cell interfaces in plant tissues is currently a missing step in the existing image analysis pipelines. We propose a method for optimizing the quality of such a mesh representation of the tissue simultaneously along several different citeria, starting from a low-quality mesh. An iterative process minimizes an energy functional defined over this discrete structure, by deforming its geometry and updating its connectivity at fixed complexity. This optimization results in a light discrete representation of the cell surfaces that enables fast visualization, and quantitative analysis, and gives way to in silico physical and mechanical simulations on real-world data. We also propose a complete quantitative evaluation scheme to measure the quality of the cell tissue reconstruction, that demonstrates the capacity of our method to fit multiple optimization criteria.
Guillaume Cerutti, Christophe Godin

Bioinformatics Models, Methods and Algorithms

Frontmatter
Latent Forests to Model Genetical Data for the Purpose of Multilocus Genome-Wide Association Studies. Which Clustering Should Be Chosen?
Abstract
The aim of genetic association studies, and in particular genome-wide association studies (GWASs), is to unravel the genetics of complex diseases. In this domain, machine learning offers an attractive alternative to classical statistical approaches. The seminal works of Mourad et al. [1] have led to the proposal of a novel class of probabilistic graphical models, the forest of latent trees (FLTM). The design of this model was motivated by the necessity to model genetical data at the genome scale, prior to a multilocus GWAS. A multilocus GWAS fully exploits information about the complex dependences existing within genetical data, to help detect the loci associated with the studied pathology. The FLTM framework also allows data dimension reduction. The FLTM model is a hierarchical Bayesian network with latent variables. Central to the FLTM construction is the recursive clustering of variables, in a bottom up subsuming process. This article focuses on the analysis of the impact of the choice of the clustering method used in the FLTM learning algorithm, in a GWAS context. We rely on a real GWAS data set describing 41400 variables for each of 3004 controls and 2005 cases affected by Crohn’s disease, and compare the impact of three clustering methods. We compare statistics about data dimension reduction as well as trends concerning the ability to split or group putative causal SNPs in agreement with the underlying biological reality. To assess the risk of missing significant association results due to subsumption, we also compare the clustering methods through the corresponding FLTM-based GWASs. In the GWAS context and in this framework, the choice of the clustering method does not influence the satisfying performance of the GWAS.
Duc-Thanh Phan, Philippe Leray, Christine Sinoquet
Crosstalk Network Biomarkers of a Pathogen-Host Interaction Difference Network from Innate to Adaptive Immunity
Abstract
Crosstalks between host and pathogen are crucial in the infection process. To obtain insight into the defense mechanisms of the host and the pathogenic mechanisms of the pathogen, pathogen-host interactions in the infection process have become a novel and promising research subject in the field of infectious disease. In this study, two pathogen-host dynamic crosstalk networks were constructed to investigate the transition of pathogenic and defensive mechanisms from the innate to adaptive immune system in the entire infection process based on two-sided time course microarray data of C. albicans-zebrafish infection model and database mining. Potential crosstalk network biomarkers for the transition from innate to adaptive immunity were identified based on proteins with larger interaction variations inside the host and pathogen, and at the interface between the host and pathogen. The crosstalk network biomarkers consist of proteins with larger interaction variation scores in the pathogen-host interaction difference network. From the crosstalk network biomarkers, the molecular mechanisms of innate and adaptive immunity were successfully investigated from a systems biology perspective. In view of these results, the proposed crosstalk network biomarkers may serve as potential therapeutic targets of infectious diseases.
Chia-Chou Wu, Bor-Sen Chen
Template Scoring Methods for Protein Torsion Angle Prediction
Abstract
Prediction of backbone torsion angles provides important constraints about the 3D structure of a protein and is receiving a growing interest in the structure prediction community. In this paper, we introduce a three-stage machine learning classifier to predict the 7-state torsion angles of a protein. The first two stages employ dynamic Bayesian and neural networks to produce an ab-initio prediction of torsion angle states starting from sequence profiles. The third stage is a committee classifier, which combines the ab-initio prediction with a structural frequency profile derived from templates obtained by HHsearch. We develop several structural profile models and obtain significant improvements over the Laplacian scoring technique through: (1) scaling templates by integer powers of sequence identity score, (2) incorporating other alignment scores as multiplicative factors (3) adjusting or optimizing parameters of the profile models with respect to the similarity interval of the target. We also demonstrate that the torsion angle prediction accuracy improves at all levels of target-template similarity even when templates are distant from the target. The improvement is at significantly higher rates as template structures gradually get closer to target.
Zafer Aydin, David Baker, William Stafford Noble
Evaluating the Robustness of Correlation Network Analysis in the Aging Mouse Hypothalamus
Abstract
Volumes of high-throughput assays been made publicly available. These massive repositories of biological data provide a wealth of information that can harnessed to investigate pressing questions regarding aging and disease. However, there is a distinct imbalance between available data generation techniques and data analysis methodology development. Similar to the four “V’s” of big data, biological data has volume, velocity, heterogeneity, and is prone to error, and as a result methods for analysis of this “biomedical big data” have developed at a slower rate. One promising solution to this multi-dimensional issue are network models, which have emerged as effective tools for analysis as they are capable of representing biological relationships en masse. Here we examine the need for development of standards and workflows in the usage of the correlation network model, where nodes and edges represent correlation between expression pattern in genes. One structure identified as biologically relevant in a correlation network, the gateway node, represents genes that change in co-expression between two different states. In this research, we manipulate parameters used to identify the gateway nodes within a given dataset to determine the consistency of results among network building and clustering approaches. This proof-of-concept is extremely important to investigate as there is a growing pool of methods used for various steps in our network analysis workflow, causing a lack of robustness, consistency, and reproducibility. This research compares the original gateway nodes analysis approach with manipulation in (1) network creation and (2) clustering analysis to test the consistency of structural results in the correlation network. To truly be able to trust these approaches, it must be addressed that even minor changes in approach can have sweeping effects on results. The results of this study allow the authors to call for stronger studies in benchmarking and reproducibility in biomedical “big” data analyses.
Kathryn M. Cooper, Stephen Bonasera, Hesham Ali
Machine Reading for Extraction of Bacteria and Habitat Taxonomies
Abstract
There is a vast amount of scientific literature available from various resources such as the internet. Automating the extraction of knowledge from these resources is very helpful for biologists to easily access this information. This paper presents a system to extract the bacteria and their habitats, as well as the relations between them. We investigate to what extent current techniques are suited for this task and test a variety of models in this regard. We detect entities in a biological text and map the habitats into a given taxonomy. Our model uses a linear chain Conditional Random Field (CRF). For the prediction of relations between the entities, a model based on logistic regression is built. Designing a system upon these techniques, we explore several improvements for both the generation and selection of good candidates. One contribution to this lies in the extended flexibility of our ontology mapper that uses an advanced boundary detection and assigns the taxonomy elements to the detected habitats. Furthermore, we discover value in the combination of several distinct candidate generation rules. Using these techniques, we show results that are significantly improving upon the state of art for the BioNLP Bacteria Biotopes task.
Parisa Kordjamshidi, Wouter Massa, Thomas Provoost, Marie-Francine Moens
Validation Study of a Wave Equation Model of Soft Tissue for a New Virtual Reality Laparoscopy Training System
Abstract
Despite the benefits of laparoscopic procedures for the patients, this technique comes with a number of environmental limitations for the surgeon, which therefore require distinctive psychomotor skills. VR training systems aim to improve these skills. For effective transference of skills from these training systems, it is important to mimic the surgical environment; including the soft tissue models. This study introduces a novel two dimensional wave equation model to mimic the interactions between soft tissue and laparoscopic tools. This model accounts for mechanical and material properties of the soft tissue. This study also proposes a new face validation technique, for an objective analysis of the developed model as a viable soft tissue model. The statistical analyses and computational cost support the use of wave equation as a replacement for present models. In the future, this model will be applied to a novel VR surgical training system for an enhanced training experience.
Sneha Patel, Jackrit Suthakorn

Bio-inspired Systems and Signal Processing

Frontmatter
Exploring the Relationship Between Characteristics of Ventilation Performance and Response of Newborns During Resuscitation
Abstract
Birth asphyxia is one of the leading causes of newborn deaths in low resource settings. In non-breathing newborns, ventilation should commence within the first minute after birth. Ventilation signals were studied and parameterized to reflect the characteristics of the provided ventilation. The effectiveness of ventilation was characterized by changes in Apgar score and heart rate. A framework for exploring the association between ventilation parameters and the effectiveness of ventilation is proposed. A statistical hypothesis test method was used to calculate p-values for different patient groups for some ventilation parameters. The results show some low p-values indicating the possible correlation between the corresponding ventilation parameters and the outcome of the treatment.
Huyen Vu, Trygve Eftestøl, Kjersti Engan, Joar Eilevstjønn, Ladislaus Blacy Yarrot, Jørgen E. Linde, Hege Ersdal
A Novel Application of Universal Background Models for Periocular Recognition
Abstract
In recent years the focus of research in the fields of iris and face recognition has turned towards alternative traits to aid in the recognition process under less constrained acquisition scenarios. The present work assesses the potential of the periocular region as an alternative to both iris and face in such conditions. An automatic modeling of SIFT descriptors, using a GMM-based Universal Background Model method, is proposed. This framework is based on the Universal Background Model strategy, first proposed for speaker verification, extrapolated into an image-based application. Such approach allows a tight coupling between individual models and a robust likelihood-ratio decision step. The algorithm was tested on the UBIRIS.v2 and the MobBIO databases and presented state-of-the-art performance for a variety of experimental setups.
João C. Monteiro, Jaime S. Cardoso
Fusion and Comparison of IMU and EMG Signals for Wearable Gesture Recognition
Abstract
We evaluate the performance of a wearable gesture recognition system for arm, hand, and finger motions, using the signals of an Inertial Measurement Unit (IMU) worn at the wrist, and the Electromyogram (EMG) of muscles in the forearm. A set of 12 gestures was defined, similar to manipulatory movements and to gestures known from the interaction with mobile devices. We recorded performances of our gesture set by five subjects in multiple sessions. The resulting data corpus is made publicly available to build a common ground for future evaluations and benchmarks. Hidden Markov Models (HMMs) are used as classifiers to discriminate between the defined gesture classes. We achieve a recognition rate of 97.8 % in session-independent, and of 74.3 % in person-independent recognition. We give a detailed analysis of error characteristics and of the influence of each modality to the results to underline the benefits of using both modalities together.
Marcus Georgi, Christoph Amma, Tanja Schultz
Integrating User-Centred Design in the Development of a Silent Speech Interface Based on Permanent Magnetic Articulography
Abstract
A new wearable silent speech interface (SSI) based on Permanent Magnetic Articulography (PMA) was developed with the involvement of end users in the design process. Hence, desirable features such as appearance, portability, ease of use and light weight were integrated into the prototype. The aim of this paper is to address the challenges faced and the design considerations addressed during the development. Evaluation on both hardware and speech recognition performances are presented here. The new prototype shows a comparable performance with its predecessor in terms of speech recognition accuracy (i.e. ~ 95 % of word accuracy and ~ 75 % of sequence accuracy), but significantly improved appearance, portability and hardware features in terms of miniaturization and cost.
Lam A. Cheah, James M. Gilbert, Jose A. Gonzalez, Jie Bai, Stephen R. Ell, Michael J. Fagan, Roger K. Moore, Phil D. Green, Sergey I. Rychenko

Health Informatics

Frontmatter
Who Is 1011011111 $$\ldots $$ 1110110010? Automated Cryptanalysis of Bloom Filter Encryptions of Databases with Several Personal Identifiers
Abstract
We provide the first efficient cryptanalysis of Bloom filter encryptions of a database containing more than one personal identifier. The cryptanalysis is fully automated and shows several drawbacks of existing encryption methods based on Bloom filters. In particular, the special representation of the hash functions as linear combinations of two hash functions f and g is exploited in order to detect Bloom filter encryptions of single bigrams (so-called atoms). The assignment of atoms to bigrams is obtained via a modification of an algorithm which was originally proposed for the automated cryptanalysis of simple substitution ciphers. Using our approach, we were able to reconstruct 77.7 % of the identifier values correctly. We point to further improvements of the basic Bloom filter approach that are worth being investigated with respect to their privacy guarantees in future work.
Martin Kroll, Simone Steinmetzer
Patient Feedback Design for Stroke Rehabilitation Technology
Abstract
The use of technology in stroke rehabilitation is increasingly common. An important aspect in stroke rehabilitation is feedback towards the patient, but research on how such feedback should be designed in stroke rehabilitation technology is scarce. Therefore, in this paper we describe an exploratory process on the design, implementation and evaluation of a patient feedback module for TagTrainer: an interactive stroke rehabilitation technology. From this process, and from previous literature, we derive five guidelines for patient feedback design in stroke rehabilitation technology. Finally, we illustrate how these guidelines can be used to evaluate existing patient feedback solutions.
Daniel Tetteroo, Lilha Willems, Panos Markopoulos
A Method and Tool for Strategic Hospital Planning
Abstract
We developed a visualization tool and a methodology to support strategic planning of hospital service portfolios. Hospitals in Switzerland are reimbursed with a fixed fee per case. The fixed-fee model makes medical services comparable from a financial point of view. In order to take advantage of this model, the data that characterizes the medical services must be operationalized. The method that we developed, centers around a visual metaphor that provides the basis for strategic thinking. It is complemented by a visualization tool that allows visualization, analysis, and modification of service portfolios. Special features enable the tool to be used during live planning sessions. We describe the method, the tool, and its application in strategy workshops for infrastructure planning, reorganization, and resource optimization decisions.
Dominique Brodbeck, Markus Degen, Andreas Walter, Serge Reichlin, Christoph Napierala
Automatic Analysis of Lung Function Based on Smartphone Recordings
Abstract
Over 250 million people, worldwide, are affected by chronic lung conditions such as Asthma and COPD. These can cause breathlessness, a harsh decrease in quality of life and, if left undetected or not properly managed, even death. In this paper, we approached part of the lines of development suggested upon earlier work. This concerned the development of a system design for a smartphone lung function classification app, which would only use recordings from the built-in microphone. A more systematic method to evaluate the relevant combinations of methods was devised and an additional set of 44 recordings was used for testing purposes. The previous 101 were kept for training the models. The results enabled to further reduce the signal processing pipeline leading to the use of 6 envelopes, per recording, half of the previous amount. An analysis of the classification performances is provided for both previous tasks: differentiation into Normal from Abnormal lung function, and between multiple lung function patterns. The results from this project encourage further development of the system.
João F. Teixeira, Luís F. Teixeira, João Fonseca, Tiago Jacinto
Towards a Safer and More Optimal Treatment of the Supracondylar Humerus Fracture
Abstract
Treating the supracondylar humerus fracture, a very common elbow’s injury, can be very challenging for pediatric orthopedic surgeons. Actually, using the pinning technique to treat it leads sometimes to many neurological and vascular complications. Furthermore, the medical staff faces a serious danger when performing such surgeries because of the recurrent exposure to harmful radiations emitted by the fluoroscopic C-arm. Considering these issues, a national project was launched to create a new robotic platform, baptized BROS, to automate the supracondylar humerus fracture’s treatment and remedy the said issues. This chapter introduces this new robotic platform and uses a real case to prove the relevance and safety of BROS.
Mohamed Oussama Ben Salem, Olfa Mosbahi, Mohamed Khalgui, Georg Frey
Real-Time Fuzzy Monitoring of Sitting Posture: Development of a New Prototype and a New Posture Classification Algorithm to Detect Postural Transitions
Abstract
In a previous work, a chair prototype was used to detect 11 standardized siting postures of users, using just 8 air bladders (4 in the chair’s seat and 4 in the backrest) and one pressure sensor for each bladder. In this paper we describe the development of a new prototype, which is able to classify 12 standard postures with an overall score of 80.9 % (using a Neural Network Algorithm). We tested how this Algorithm worked during postural transitions (frontal and lateral flexion) and in intermediate postures, identifying some limitation of this Algorithm. This prompted the development of a Posture Classification Algorithm based on Fuzzy Logic and is able to determine if the user is adopting a good or a bad posture for specific time periods, using as input the Centre of Pressure, the Posture Adoption Time and the Posture Output from the existing Neural Network Algorithm. This newly developed Classification Algorithms is advancing the development of new Posture Correction Algorithms based on Fuzzy Actuators.
Leonardo Martins, Bruno Ribeiro, Hugo Pereira, Rui Almeida, Jéssica Costa, Cláudia Quaresma, Adelaide Jesus, Pedro Vieira
TogetherActive - Key Concepts and Usability Study
Abstract
Despite the well-known benefits of physical activity on health, the recommended level of physical activity is not reached by everyone. Many interventions are aimed at reducing sedentary behaviour and increasing physical activity. As an intervention, we developed a virtual community system, TogetherActive, aiming at providing the social support to people in their daily life. Typically, virtual communities provide emotional and/or informational support, but our contribution aims mainly the instrumental and appraisal support. The community is coupled with physical activity sensor. In this system we focused on concepts such as individual and group goals, comparison, competition and cooperation in order to increase motivation to meet the daily recommended physical activity level. In this paper presents the design and the key concepts of the TogetherActive system, its implementation and the usability study.
Lamia Elloumi, Bert-Jan van Beijnum, Hermie Hermens
Analysis of Eye Movements with Eyetrace
Abstract
In the time of affordable and comfortable video-based eye tracking, the need for analysis software becomes more and more important. We introduce Eyetrace, a new software developed for the analysis of eye-tracking data during static image viewing. The aim of the software is to provide a platform for eye-tracking data analysis which works with different eye trackers, offering thus the possibility to compare results beyond the specific characteristics of the hardware devices. Furthermore, by integrating various state-of-the-art and new developed algorithms for analysis and visualization of eye-tracking data, the influence of different analysis steps and parameter choices on typical eye-tracking measures is totally transparent to the user. Eyetrace integrates several algorithms to identify fixations and saccades, and to cluster them. Well-established algorithms can be used side-by-side with bleeding-edge approaches with a continuous visualization. Eyetrace can be downloaded at http://​www.​ti.​uni-tuebingen.​de/​Eyetrace.​1751.​0.​html and we encourage its use for exploratory data analysis and education.
Thomas C. Kübler, Katrin Sippel, Wolfgang Fuhl, Guilherme Schievelbein, Johanna Aufreiter, Raphael Rosenberg, Wolfgang Rosenstiel, Enkelejda Kasneci
Backmatter
Metadaten
Titel
Biomedical Engineering Systems and Technologies
herausgegeben von
Ana Fred
Hugo Gamboa
Dirk Elias
Copyright-Jahr
2015
Electronic ISBN
978-3-319-27707-3
Print ISBN
978-3-319-27706-6
DOI
https://doi.org/10.1007/978-3-319-27707-3

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