Advanced Sensors for Biomedical Applications

Recognizing human actions and analyzing human behaviors from accelerometer data has become a challenging task. Hence, Human Action Recognition (HAR) using inertial sensors have been addressed in a plethora of various review papers. This chapter provides a detailed state of art survey of HAR exploiting acceleration data. Considering different modalities, we prove that the accelerometer is one of the most promising sensors in this field by presenting an overview of its applications. In addition, we propose a comprehensive review of recent studies in this domain along different views: from data modalities to feature extraction and classification steps. Moreover, we list the most publicly available databases that include accelerometer data. Afterwards, we used a multi-level fusion framework that includes signal-level, feature-level, score level and the decision level fusion in order to improve the recognition performance. For the classification, we took advantage of the support vector machine with features from the time-frequency domain. The proposed framework was evaluated using three public datasets: WARD, MHAD and Realdisp. The results obtained from the fusion techniques indicate that the score level provides a satisfactory performance compared to the other levels and with the use of each accelerometer separately.

Gait analysis plays an important role in various applications such as health care, clinical rehabilitation, sport training and pedestrian navigation. In order to monitor the human gait, an interesting approach is to analyze the foot plantar pressure distribution between the foot and the ground. In recent years, the emergence of flexible, soft and lightweight sensors facilitates the rapid technological advances in in-shoe foot pressure measurements, thereby especially carbon nanotubes-based sensors provide an outstanding solution for the implementation of flexible, soft pressure sensors in foot pressure distribution analysis. This chapter focuses on the design and implementation of multiwalled carbon nanotubes (CNT)/polydimethylsil-oxane (PDMS) based nanocomposite pressure sensors for the analysis of the foot pressure distribution. The sensor is durable, stable and shows sensitivity of 3.3 k\({\Omega }\)/kPa and hysteresis smaller than 3.64% with maximum detectable pressure up to 217 kPa, which is suitable for the measurement of human foot pressure. The proposed sensor has been implemented in a flexible in-sole, which is designed based on normal arch foot anatomy. A total of 12 sensors are distributed in the heel, lateral back foot, midfoot and front foot. The foot pressure distribution for different persons while walking and standing using nanocomposite sensor based in-sole were investigated by measuring the changing in resistance of the pressure sensors, when pressure applied on it. It shows that foot pressure distribution is higher in the fore foot and the heel while person standing in normal position. While walking, initially the foot pressure is in the heel and then transferred to the entire foot and finally it is concentrated on the fore foot.

Nowadays, the demand for flexible and wearable devices is significantly increasing. Thereby, sensors based on carbon materials are gaining importance due to their high flexibility, sensitivity and medical compatibility. Precisely, nanocomposite based pressure and strain sensors present an interesting potential of applied force detection that helps to build the basis for body attached sensor networks. These sensor principles based on polymer carbon nanotubes composites (PCN) will have the capability for tracking finger movements, gestures and grasping. Therefore, several studies are explored in hand muscle rehabilitation, sign communication and robotic telemanipulation. This chapter reviews developed carbon materials sensors and integrated solutions for hand gestures/forces detection in the biomedical application and robotic telemanipulation. In this context, a novel PCN strain and pressure sensors were presented and investigated. An SBS (styrene-butadiene-styrene rubber)/C-TPU (conductive thermoplastic urethane) strain sensor with 1 mm as the diameter is developed. The proposed sensor shows promising sensitivity and stretchability performances with up to 50% of strain and gauge factor equal to 24. In the other part, Poly-Dimethylsiloxane (PDMS)/Multiwalled carbon nanotubes (MWCNTs) pressure sensors are investigated. The results demonstrate excellent sensing performance e.g. fast response to detect low pressure, high durability after 100 cyclic loading/unloading test and high sensitivity up to 670 kPa. Moreover, a hybrid hand motion detection system was implemented for hand rehabilitation and gestures detection. The proposed sensors were attached to a glove that leads to the monitoring of fingers’ movements and the palm pressure distribution.

Wireless Body Sensor Networks (WBSNs) are widely used in Internet of Things (IoT) based health care technologies, where the health status of patients can be monitored through a group of small-powered and lightweight sensor nodes. Energy consumption is thereby a major issue. One of the reasons for energy losses in WBSN are retransmission process, when data collision takes place or data are not received properly due to channel fading. In order to reduce the necessity for data retransmission, we propose to reduce the transmitted data by applying data aggregation techniques. This raises also the network lifetime by minimizing the resources consumption of the sensor nodes. Nevertheless, it may degrade significantly the service quality metrics, such as data reliability and communication security. In this chapter, an accurate data classification model for multiple health signals based on different machine learning algorithms is proposed to ensure the reliability of data aggregation. The performance evaluation shows that the Random Forest algorithm is the best classifier in terms of accuracy (97%) and sensitivity (92%) under general conditions.

Day by day, the ability to detect and to identify automatically the objects among images and videos without constraint has becoming more and more important. The security systems, robots, smartphones and smart devices need to know the semantic meaning of image. The increase in object detection and identification algorithms is essentially related to the increase in complex object specification and authentication techniques. This could be resolved only when using the parallel architectures that can support heavy parallel processing such as GPU. In this chapter, we propose to present an implementation of moving humans detection algorithm on GPU based on the programming language CUDA. We proposed an implementation of an algorithm to extract the image features using the Fourier descriptor on GPU. We have proposed a second implementation to extract the image features based on the HOG descriptor on GPU. To detect the moving objects, we have implemented a background subtraction algorithm based on the GMM: Gaussian Mixture Model on GPU. In order to integrate these implementations in the main moving humans detection algorithm, the use of preprocessing and filtering techniques is necessary at this level as well as the CCL: Connected Component Labeling method which allows extracting the Moving objects from the rest of the image. The implementation of such kind of algorithm on GPU allows a great performance in terms of execution time.

Non-invasive monitoring of human health is of a high importance for early detection of illnesses and improving life quality. Breath monitoring is important for detection of severe diseases such as lung cancer or sleep apnea. In this work, we introduce a breath sensor based on a graphene oxide film deposited on silver interdigitated electrode and a flexible substrate. The graphene oxide film was then thermally annealed to partially reduce the graphene oxide. The measurements of sensor impedance carried out at different humidity levels show a high decrease by several orders of magnitudes by increasing the relative humidity. Sensitivity to humid air results from the high hydrophilicity of the graphene oxide due to its oxygen functional groups. The change of transport mechanism from Nyquist plot shows the change of the sensor impedance from the capacitive behavior to a semicircle of parallel resistance and capacitance. The sensors show an ultrahigh sensitivity to humidity at high humidity values, a very low response time of less than one second and an excellent repeatability of the measurements. For tracking human breathing, the reaction on natural breathing was acquired by a digital oscilloscope together with an IoT mobile application to visualize the results in real time and store them for further processing. The sensor performance shows that it is suitable as a noninvasive and flexible breath-monitoring sensor system. The proposed sensor can be a step to flexible and cheap wearable sensors for detection of human breath and hazardous breath airborne such as COVID-19.

In this chapter, we describe the development and fabrication of gold microelectrodes based on silicon by silicon technology, for multiplex detection of cytokines. Cytokines have become a crucial biomarker for the identification of end-stage heart failure (ESHF) for patients during early phase of left ventricular assisted device (LVAD) implantation. The microelectrode device consists of three gold working microelectrodes that were activated and 4-aminophenylacetic acid (CMA) was electroaddressed onto individual gold WEs. The carboxylic acid functionalities of the diazotated aromatic amine were activated through carbodiimide chemistry and anti-interleukin-10 monoclonal antibodies (anti-IL-10 mAb) were immobilized onto the transducers surface. The interaction between the antibody-antigen (Ab-Ag) was characterized by electrochemical impedance spectroscopy (EIS). Here, Nyquist plots have shown a stepwise variation due to the charge transfer resistance (Rct) between the Ab activated surfaces with the detection of the human IL-10. For early expression monitoring, commercial proteins of human IL-10 were analyzed between 1 pg/mL and 100 pg/mL. The protein concentrations within the linear range of 1–50 pg/mL were detected and these values formulated a sensitivity of 0.008 (pg/mL)\(^{-1}\) (\(R^2 = 0.9840\)). These preliminary results demonstrated that the developed biosensor was sensitive to the detection of human IL-10 and the calculated limit of detection (LOD) was measured at 0.156 (pg/mL)\(^{-1}\). To validate the biosensors response, the experiment was repeated several times on different gold working WEs by applying the same conditions. The overall relative standard deviation percentage (% R.S.D.) was 4.9% which demonstrates the successful fabrication for the detection of human IL-10 through diazonium salt electroreduction.

Prostate cancer (PCa) is the first most frequently diagnosed malignancy in man in Europe and is the third major case of males’ cancer-related death. PCa screening and diagnosis are therefore societal and public health issues. Prostate specific antigen is the routine marker, but it is not specific for PCa. Several promising new biomarkers, including proteins, circulating tumor-derived DNA and RNA, and metabolites, are currently under clinical and analytical evaluation. The most promising ones are probably those present in urine, a valuable biological fluid that contains diverse biomarkers produced nearby by the prostatic tumour, and which can be easily collected with non-invasive sampling procedure. For each type of biomarker, there are already conventional assay techniques: namely ELISA and Western Blot for proteins and RT-PCR for DNA and RNA. Despite their undeniable metrological performances, these techniques remain expensive and require sophisticated equipment. Hence there is a need for ultra-sensitive, reliable and disposable tools for preclinical diagnosis of PCa. The most promising candidates are probably the electrochemical biosensors. In this review are thus presented the recent advances in the design of electrochemical biosensors for the quantification of urinary biomarkers of prostate cancer.

Recently, microRNAs gain a great interest in the bio-molecular field due to their fundamental role in clinical diagnostics. In this chapter, we generally discussed the biogenesis of microRNAs and the progress made for their detection. Researchers have widely tried to investigate their effort to build a sensitive, selective, and accurate platform for microRNA detection. To date, multiple techniques have been developed, ranging from the old conventional method (northern blot, RT-PCR, microarrays) to the newly established ones (biosensors, nanopores). However, given the various challenges related to miRNA detection, such as low abundance, small size, and high level of sequence similarity, different enzymatic and non-enzymatic amplification approaches were successfully exploited to improve such devices’ sensitivity. Among these strategies, HCR, RCA, nanomaterials, and the use of enzyme-based target recycling like DSN enzyme. On the other hand, the combination of different methods has emerged as an ideal option for further enhancement of the sensitivity. In the end, knowing that the expression of a single miRNA is not enough to identify one specific disease, it is usually necessary to implant a simultaneous and multiplexed technique for more sophisticated and efficient diagnostic tools.

Helicobacter Pylori (HP) bacteria is considered as one of the most capable pathogens in colonizing the human gastrointestinal tract. It is a dangerous carcinogen bacterium that infects about 50% of humans. Infection with HP may begin during childhood and can persist lifelong. Thus, detecting HP at an early stage is very important to prevent developing symptoms. Consequently, reliable detection techniques of HP for in-vitro samples are very important. In-vitro detection can include contaminated food, water, public sanitary facility, and any contaminated environment. Several techniques are available for detection of HP in complex samples, such as polymerase chain reaction (PCR) test and urea breath test (UBT). However, these techniques usually need excessive sample processing, complex instruments, trained personnel, and long detection time. Nowadays, detection of pathogens in complex samples is becoming very important as they are becoming more resistant to antibiotics. In this contribution we review detection methods of HP and address thereby important findings in stool antigen tests, fluorescent detection methods, calorimetric detection methods, Surface Plasmon Resonance detection methods as well as electrochemical methods. In addition, we provide perspectives for future developments in this important and challenging field.