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Accurate diagnosis and classification of epileptic seizures can greatly support patient treatments. As many epileptic seizures are convulsive and have a motor component, the analysis of muscle activity can provide valuable information for seizure classification. Therefore, this paper present a feasibility study conducted on healthy volunteers, focusing on tracking epileptic seizures movements using surface electromyography signals (sEMG) measured on human limb muscles. For the experimental studies, first, compact wireless sensor nodes were developed for real-time measurement of sEMG on the gastrocnemius, flexor carpi ulnaris, biceps brachii, and quadriceps muscles on the right side and the left side. For the classification of the seizure, a machine learning model has been elaborated. The 16 common sEMG time-domain features were first extracted and examined with respect to discrimination and redundancy. This allowed the features to be classified into irrelevant features, important features, and redundant features. Redundant features were examined with the Big-O notation method and with the average execution time method to select the feature that leads to lower complexity and reduced processing time. The finally selected six features were explored using different machine learning classifiers to compare the resulting classification accuracy. The results show that the artificial neural network (ANN) model with the six features: IEMG, WAMP, MYOP, SE, SKEW, and WL, had the highest classification accuracy (99.95%). A further study confirms that all the chosen eight sensors are necessary to reach this high classification accuracy.

This work presents a systematic review to provide an overview of the possibilities for coupling, fabrication or embedding of electronics into textiles whilst assuring the capability of these products to meet the requirements of a sports modality. The development of smart wearables systems for sports based on textiles attracts more and more users – motivated by design, by technology, as well as by the expectation of increased performance. A bibliographic search was carried out using the following databases: Scopus, Web of Science, IEEE Xplore and Science Direct. This study includes 32 articles and discusses these in a new taxonomy with three dimensions: measured variable, types of feedback and applications. Of the 23 technologies surveyed, this review showed that these wearable systems are mainly used for vital signs monitoring and to provide feedback on the electrical activity of the heart, with sensors mostly placed in the chest. Usually, the technologies are externally attachable rather than embedded in the textile. We observed that the implementation of design as the process of development of e-textile products is still only scarcely present in these studies., 2C2T - Centre for Textile Science and Technology, TSSiPRO - Technologies for Sustainable and Smart Innovative Products, NORTE-01-0145-FEDER-000015, Lab2PT - Landscape, Heritage and Territory Laboratory

In human-robot interaction, sensors are relevant in guaranteeing stability and high performance in real-time applications. Nonetheless, accuracy and portable sensors for robots usually have high costs and little flexibility to process signals with free software. Therefore, we propose a wearable sensor network to measure lower limb angular position in human-robot interaction systems. The methodology employed to achieve the aim consisted in implementing a wireless network using low-cost devices, verifying design requirements, and making a validation via a proof of concept. The requirements to design the network include low loss of information, real-time communication, and sensor fusion to estimate the angular position using a gyroscope and accelerometer. Hence, the sensor network developed has a client-server architecture based on ESP8266 microcontrollers. In addition, this network uses the standard 802.11 b/g/n to transmit angular velocity and acceleration measures. Furthermore, we implement the user datagram protocol (UDP) protocol to operate in real-time with a sample time of 10 ms. Finally, we implement a proof of concept to show the system’s effectiveness. Thus, we use the Kalman filter to estimate the angular position of the foot, shin, thigh, and hip. Results indicate that the implemented sensor network is suitable for real-time robotic applications.

Wearable systems are pervading our lives in several applications: from fitness to sport, from health monitoring to rehabilitation, up to prosthetics and empowering human functions through exoskeletons. If the technological requirements are mainly quantitative and easy to measure, their usability, acceptance, and user experience are generally poorly studied. There is a lack of a structured methodological approach to develop a comprehensive protocol. This paper aimed at providing these methodological bases and at defining some of the related tools. The first action was to clearly define the objectives of the study: (a) to identify design inconsistencies and usability problems or errors; (b) to validate the use of wearable systems under controlled test conditions with representative users; and (c) to establish a baseline in terms of user performance and user satisfaction levels. A five-step approach should be adopted: (1) define the target users; (2) conduct a task analysis for identifying the context, the parameters to be measured, and the methodology to collect data; (3) prepare a protocol and the investigation tools; (4) execute the usability experiments; and (5) analyze and report the data. This segmentation of the complex task of usability measurement into single steps can help in elaborating a proper protocol where users, usability factors and parameters, and their recording tools (questionnaires or measurement methods) are correctly identified and prepared for the experimental activity. The application of this methodology can support researchers, developers, and users in improving the deployment of these devices in our lives and the exploitation of these systems for increasing our quality of life.

The future communication will be highly data-driven, enabled by unlimited wireless connectivity. The emerging technologies are expected to fulfill the expectations not achieved with 4G, also the new technologies fusing artificial intelligence inspired applications in every corner of the world, such as the Internet of Things (IoT) with pervasive wireless connectivity. As one of the core techniques in 5G, the IoT will play a leading role in upcoming telecommunications, wearable systems, and unmanned vehicles. The innovative technologies employing interdisciplinary techniques are also expected to take advantage of the recent advancement in functional materials, semiconductor technologies, and metamaterials. Metamaterials are the materials of the future. Metamaterials are engineered materials whose electromagnetic and optical properties are determined by their structural design especially the unit cells. By the combination of different microscopic unit cell elements into large-scale designs, a new structure can be designed and fabricated with engineered properties required for new technologies. The presence of metamaterials has offered tunability, flexibility in design and has resulted in the enhancement of qualitative parameters of various electromagnetic structures like Antennas, filters, and resonators. In this review article, we focus on the concept, modeling, and advanced design approaches of metamaterials in the antenna structure. A comparative study of these structures with recent advancements and a futuristic approach is discussed at the end of the article, which will be beneficial for the young researchers.

BACKGROUND: The modern Internet of Things (IoT) makes small devices that can sense, process, interact, connect devices, and other sensors ready to understand the environment. IoT technologies and intelligent health apps have multiplied. The main challenges in the sports environment are playing without injuries and healthily. OBJECTIVE: In this paper the Internet of Things-based Smart Wearable System (IoT-SWS) is introduced for monitoring sports person activity to improve sports person health and performance in a healthy way. METHOD: Wearable systems are commonly used to capture individual sports details on a real-time basis. Collecting data from wearable devices and IoT technologies can help organizations learn how to optimize in-game strategies, identify opponents’ vulnerabilities, and make smarter draft choices and trading decisions for a sportsperson. RESULTS: The experimental result shows that IoT-SWS achieve the highest accuracy of 98.22% and efficient in predicting the sports person’s health to improve sports person performance reliably.

Textile electrodes are an alternative to conventional silver-chloride electrodes in wearable systems. Their easy integration in garments and comfort provided to the user make them an interesting development of textile engineering. The potential of such electrodes to allow more unobtrusive data collection in health and sports context may enable the development of biosensing garments to be used in biomechanics. However, proper validation of the recorded signals is paramount, and few studies have yet presented consistent methodologies for textile-based electromyographic recordings. This study presents the validation of the electrical and morphological properties of electromyographic signals recorded with textile electrode, in comparison to conventional silver-chloride electrodes. Results indicate that both sets of electrodes have identical signal-to-noise ratios, but with distinct impedance frequency responses. Electromyographic envelope morphologies are also identical, although textile electrodes usually have lower amplitudes., Los electrodos textiles son una alternativa a los electrodos de cloruro de plata convencionales en los sistemas portátiles. Su fácil integración en las vestimentas y la comodidad que brinda al usuario las convierten en un interesante desarrollo de la ingeniería textil. El potencial de tales electrodos para permitir una recopilación de datos más discreta en el contexto de la salud y el deporte puede permitir el desarrollo de vestimentas biosensibles para su uso en biomecánica. Sin embargo, la validación adecuada de los señales registradas es primordial y pocos estudios han presentado metodologías consistentes para registros electromiográficos basados en textiles. Este estudio presenta la validación de las propiedades eléctricas y morfológicas de señales electromiográficas registradas con electrodo textil, en comparación con electrodos convencionales de cloruro de plata. Los resultados indican que ambos los electrodos tienen relaciones señal-ruido idénticas, pero con una respuesta de frecuencia de impedancia distinta. La morfología de la envoltura electromiográfica también se identifica entre electrodos, aunque generalmente de menor amplitud en el electrodo textil., Eléctrodos têxteis são uma alternativa aos convencionais eléctrodos de cloreto de prata em sistemas vestiveis. A sua fácil integração em vestuário e o conforto que providenciam tornam-nos um interessante desenvolvimento da engenharia têxtil. O potencial destes eléctrodos para permitir a discreta recolha de dados em contexto de saúde e desporto poderá permitir o desenvolvimento de vestuário biossensivel com aplicação em biomecânica. No entanto, uma adequada validação dos sinais registados é fundamental, e poucos estudos apresentaram metodologias consistentes para registos electromiográficos baseados em elétrodos têxteis. Este estudo apresenta a validação das propriedades eléctricas e morfológicas de sinais electromiográficos registados com eléctrodos têxteis, em comparação com os convencionais eléctrodos de cloreto de prata. Os resultados indicam que ambos os eléctrodos apresentam razões sinal-ruído identicos, mas com uma resposta em frequência de impedância distincta. A morfologia do envelope electromiográfico é também identifca entre electrodos, ainda que geralmente de menor amplitude no eléctrodo têxtil.

There is a growing demand of comfortable and unobtrusive wearable systems for monitoring a variety of physiological parameters and in particular the respiratory frequency ( ${f}_{R}$ ). The most popular techniques for ${f}_{R}$ monitoring cannot be used in several clinical applications and in unstructured environment. These issues have fostered a dramatic growth of interest for wearable systems devoted to monitor ${f}_{R}$ . In this arena, fiber Bragg grating (FBG) sensors have gained due to a variety of benefits. In this work, we designed and fabricated an FBG-based wearable device for ${f}_{R}$ monitoring from the nasal airflow. The proposed design does not require a mask to improve the comfortability and acceptability of the system. The sensing element was functionalized by a hygroscopic coating material to make the FBG sensitive to relative humidity changes. This feature allows calculating ${f}_{R}$ starting from the discrimination between the inspiration and expiration phases. A pilot study on 6 volunteers was designed to assess the system during three different breathing stages (i.e., slow, normal and fast breathing). Results showed high performance of the proposed wearable device in detecting mean and breath-by-breath ${f}_{R}$ values (i.e., mean percentage errors ≤ 2.29 % and bias ≤ 0.31 breaths per minute) during slow breathing, normal breathing, and fast breathing.

Thanks to rapid technological innovation over the last decade, a wide range of wearable devices have emerged, playing an inspiring role in healthcare, diagnostics, and sports monitoring. This review summarizes recent progress in the development and application of electrochemical (EC) sweat sensors and self-powered systems for wearable applications. Presented here are the most common types of EC sweat sensors, namely potentiometric ion-selective electrodes, amperometric enzymatic and non-enzymatic sensors, as well as differential pulse voltammetric sensors. The sensing principle and novel functional nanomaterials that are used to enhance the performance of wearable sweat sensors and their applications are summarized. The recent advancement of battery-free wearable devices is presented, including self-powered biofuel cells (BFC), energy harvesting from novel materials and its strategy, are also discussed. The challenges in, and opportunities for, the development of sweat sensor-based wearable systems moving forward are reviewed, with the aim of shedding some light on future directions.

Dissertação de mestrado em Engenharia Biomédica (especialização em Eletrónica Médica), Atualmente, as Lesões Musculoesqueléticas Relacionadas com o Trabalho (LMERT) são considera das o ”problema relacionado com o trabalho mais prevalente”na União Europeia, levando a um custo estimado de cerca de 240 biliões de euros. Em casos mais severos, estes distúrbios podem causar danos vitalícios à saúde do trabalhador, reduzindo a sua qualidade de vida. De facto, LMERTs são con sideradas a principal causa da reforma precoce dos trabalhadores. Foi reportado que os segmentos da parte superior do corpo são mais suceptíveis ao desenvolvimento de LMERTs. Para mitigar a prevalência de LMERTs, ergonomistas maioritariamente aplicam métodos de avaliação observacionais, que são alta mente dependentes da experiência do analista, e apresentam baixa objetividade e repetibilidade. Desta maneira, esforços têm sido feitos para desenvolver ferramentas de avaliação ergonómica baseadas na instrumentação, para compensar essas limitações. Além disso, com a ascensão do conceito da indústria 5.0, o trabalhador humano volta a ser o foco principal na indústria, juntamente com o robô colaborativo. No entanto, para alcançar uma relação verdadeiramente colaborativa e simbiótica entre o trabalhador e o robô, este último precisa de reconhecer as intenções do trabalhador. Para superar este obstáculo, sis temas de captura de movimento podem ser integrados nesta estrutura, fornecendo dados de movimento ao robô colaborativo. Esta dissertação visa a melhoria de um sistema de captura de movimento autónomo, da parte supe rior do corpo, de abordagem inercial que servirá, não apenas para monitorizar a postura do trabalhador, mas também avaliar a ergonomia do usuário e fornecer consciencialização postural ao usuário, por meio de motores de biofeedback. Além disso, o sistema foi já idealizado tendo em mente a sua integração numa estrutura colaborativa humano-robô. Para atingir estes objetivos, foi aplicada uma metodologia de design centrado no utilizador, começando pela análise do Estado da Arte, a avaliação das limitações do sistema anterior, a definição dos requisitos do sistema, o desenvolvimento da peça de vestuário, arquite tura do hardware e arquitetura do software do sistema. Por fim, o sistema foi validado para verificar se estava em conformidade com os requisitos especificados. O sistema é composto por 9 Unidades de Medição Inercial (UMI), posicionados na parte inferior e superior das costas, cabeça, braços, antebraços e mãos. Também foi integrado um sistema de atuação, para biofeedback postural, composto por 6 motores vibrotáteis, localizados na região lombar e próximo do pescoço, cotovelos e pulsos. O sistema é alimentado por uma powerbank e todos os dados adquiridos são enviados para uma estação de processamento, via WiFi (User Datagram Protocol (UDP)), garantindo autonomia. O sistema tem integrado um filtro de fusão Complementar Extendido e uma sequência de calibração Sensor-para-Segmento estática, de maneira a aumentar a precisão da estimativa dos ângulos das articulações. Além disso, o sistema é capaz de amostrar os dados angulares a 240 Hz, enquanto que o sistema anterior era capaz de amostrar no máximo a 100 Hz, melhorando a resolução da aquisição dos dados. O sistema foi validado em termos de hardware e usabilidade. Os testes de hardware abordaram a caracterização da autonomia, frequência de amostragem, robustez mecânica e desempenho da comuni cação sem fio do sistema, em diversos contextos, e também para verificar se estes estão em conformidade com os requisitos técnicos previamente definidos, que foi o caso. Adicionalmente, as especificações da nova versão do sistema foram comparadas com a anterior, onde se observou uma melhoria direta signifi cativa, como por exemplo, maior frequência de amostragem, menor perda de pacote, menor consumo de corrente, entre outras, e com sistemas comerciais de referência (XSens Link). Testes de usabilidade foram realizados com 9 participantes que realizaram vários movimentos uniarticulares e complexos. Após os testes, os usuários responderam a um questionário baseado na Escala de Usabilidade do Sistema (EUS). O sistema foi bem aceite pelos os usuários, em termos de estética e conforto, em geral, comprovando um elevado nível de vestibilidade., Nowadays, Work-Related Musculoskeletal Disorders (WRMSDs) are considered the ”most prevalent work-related problem” in the European Union (EU), leading to an estimated cost of about 240 billion EUR. In more severe cases, these disorders can cause life-long impairments to the workers’ health, reducing their quality of life. In fact, WRMSDs are the main cause for the workers’ early retirement. It was reported that the upper body segments of the worker are more susceptible to the development of WRMSDs. To mitigate the prevalence of WRMSD, ergonomists mostly apply observational assessment methods, which are highly dependant on the analyst’s expertise, have low objectivity and repeatability. Therefore, efforts have been made to develop instrumented-based ergonomic assessment tools, to compensate for these limitations. Moreover, with the rise of the 5.0 industry concept, the human worker is once again the main focus in the industry, along with the Collaborative Robot (cobot). However, to achieve a truly collaborative relation between the worker and the cobot, the latter needs to know the worker’s intentions. To surpass this obstacle, Motion Capture (MoCap) systems can be integrated in this framework, providing motion data to the cobot. This dissertation aims at the improvement of a stand-alone, upper-body, inertial, MoCap system, that will serve to not only monitor the worker’s posture, but also to assess the user’s ergonomics and provide posture awareness to the user, through biofeedback motors. Furthermore, it was also designed to integrate a human-robot collaborative framework. To achieve this, a user-centred design methodology was applied, starting with analyzing the State of Art (SOA), assessing the limitations of the previous system, defining the system’s requirements, developing the garment, hardware architecture and software architecture of the system. Lastly, the system was validated to ascertain if it is in conformity with the specified requirements. The developed system is composed of 9 Inertial Measurement Units (IMUs), placed on the lower and upper back, head, upper arms, forearms and hands. An actuation system was also integrated, for postural biofeedback, and it is comprised of 6 vibrotactile motors, located in the lower back, and in close proximity to the neck, elbows and wrists. The system is powered by a powerbank and all of the acquired data is sent to a main station, via WiFi (UDP), granting a standalone characteristic. The system integrates an Extended Complementary Filter (ECF) and a static Sensor-to-Segment (STS) calibration sequence to increase the joint angle estimation accuracy. Furthermore, the system is able to sample the angular data at 240 Hz, while the previous system was able to sample it at a maximum 100 Hz, improving the resolution of the data acquisition. The system was validated in terms of hardware and usability. The hardware tests addressed the char acterization of the system’s autonomy, sampling frequency, mechanical robustness and wireless commu nication performance in different contexts, and ascertain if they comply with the technical requirements, which was the case. Moreover, the specifications of the new version were compared with the previous one, where a significant direct improvement was observed, such as, higher sampling frequency, lower packet loss, lower current consumption, among others, and with a commercial system of reference (XSens Link). Usability tests were carried out with 9 participants who performed several uni-joint and complex motions. After testing, users answered a questionnaire based on the System Usability Scale (SUS). The system was very well accepted by the participants, regarding aesthetics and overall comfort, proving to have a high level of wearability.

The demand for wearable devices to simultaneously monitor heart rate (HR) and respiratory rate (RR) values has grown due to the incidence increase in cardiovascular and respiratory diseases. The use of inertial measurement unit (IMU) sensors, embedding both accelerometers and gyroscopes, may ensure a non-intrusive and low-cost monitoring. While both accelerometers and gyroscopes have been assessed independently for both HR and RR monitoring, there lacks a comprehensive comparison between them when used simultaneously. In this study, we used both accelerometers and gyroscopes embedded in a single IMU sensor for the simultaneous monitoring of HR and RR. The following main findings emerged: (i) the accelerometer outperformed the gyroscope in terms of accuracy in both HR and RR estimation; (ii) the window length used to estimate HR and RR values influences the accuracy; and (iii) increasing the length over 25 s does not provide a relevant improvement, but accuracy improves when the subject is seated or lying down, and deteriorates in the standing posture. Our study provides a comprehensive comparison between two promising systems, highlighting their potentiality for real-time cardiorespiratory monitoring. Furthermore, we give new insights into the influence of window length and posture on the systems’ performance, which can be useful to spread this approach in clinical settings.

Billions of people are using smartphones and computers with poor posture. A careless attitude towards spinal posture could be dangerous for long-term spinal health, leading eventually to curvature of the spine. Ignoring this fact and its treatment at the early stage will significantly deteriorate spinal health and force surgical intervention. Instead of developing an automated posture-correcting system, the existing research mostly focused on a posture-monitoring system to inform the users via a human interface, e.g., Bluetooth-based devices. Therefore, this paper proposes a novel posture-correction method to automatically prevent spinal disease by facilitating proper posture habits. Specifically, we develop a fluid-driven wearable posture corrector, whose skeleton can be fabricated simply using a 3D printer, to estimate angular posture deviation using sensors and provide appropriate assistance to correct the posture habit of the user. Mounted sensors provide the degree of postural bending, and a controller regulates the appropriate signals to provide a friendly pulling force as a reminder to the user through a fluid-driven actuator. The skeleton with a fluid-driven tool is designed to mimic the motion of the spinal posture by activating the actuator, which injects (or releases) the fluid into (or from) the skeleton frame and regulates forces to reduce the angular deviation of the skeleton. The 3D-printed skeleton with a flexible rubber tube has been experimentally evaluated to ensure proper actuating mechanism through the adjustment of air pressure. It is found that, by applying air pressure in the range of 0 to 101.4 kPa, the skeleton is pulled back approximately 1 N to 7 N forces, minimizing the angle up to 12.44∘ with respect to the initial steady stage, which leads to a maximum posture correction of 32.55% angle (θ) of poor posture. From the above experiments, we ensure the functionality of the proposed posture corrector in producing backward forces to correct the posture automatically.

The availability of fiber conductors that can be stretched to large extents without significantly changing resistance or conductivity could enable the advances of elastic conductors as electronic interconnects, electronic skins, stretchable sensors, wearable systems, and medical robots. Therefore, the preparation of fiber conductors with high stretchability is crucial to the development of flexible electronic devices. This review summarizes the advances in constructing fiber conductors with an emphasis on recent developments of buckled structural design, fabrication methodologies, and strategies, with the ultimate goal of achieving good stability of resistance or conductivity at large strains. This review classifies the buckled fiber conductors into inner buckling and outer buckling, and related examples are summarized, providing a context that buckled fiber conductors are geared towards applications in electrical interconnects, wearable systems, and smart medical robotics. The present challenges in this area are critically evaluated and our perspectives for improving the performance of the buckled fiber conductors for future applications are presented.

Electronic textiles promise to provide an intelligent platform to enlarge the scope of wearable electronic applications. Therefore, the combination of flexible energy storage devies into wearable systems is a key for operating these electronic textiles during bending, knotting, and rolling. Nonetheless, the application of fibrous supercapacitors consisting of a gel-electrolyte and carbon fiber electrode is still obstructed by low capacitance, low rate-performance, and poor cycling stability owing to the inefficient interface between the gel-electrolyte and electrode. Here, a fibrous supercapacitor is obtained using an optimized gelelectrolyte that improves the ionic diffusion capability. The optimized fibrous supercapacitor shows a superior electrochemical performance, including high specific capacitance of 41 mF cm−2 at current density of 2.0 μA cm−2, high-rate performance with 17 mF cm−2 at a current density of 15.0 μA cm−2, and outstanding cycling stability (88% after 3,000 cycles at a current density of 200.0 μA cm−2). The excellent energy storage performance is mainly attributed to the optimzied interface between the gelelectrolyte and electrode material, leading to an improved ionic diffusion capability.

Lightweight computing technologies such as the Internet of Things and flexible wearable systems have penetrated our everyday lives exponentially in recent years. Without a question, the running of such electronic devices is a major energy problem. Generally, these devices need power within the range of microwatts and operate mostly indoors. Thus, it is appropriate to have a self-sustainable power source, such as the photovoltaic (PV) cell, which can harvest indoor light. Among other PV cells, the dye-sensitized solar cell (DSSC) has immense capacity to satisfy the energy demands of most indoor electronics, making it a very attractive power candidates because of its many benefits such as readily available materials, relatively cheap manufacturing methods, roll-to-roll compatibility, easy processing capabilities on flexible substrates and exceptional diffuse/low-light performance. This review discusses the recent developments in DSSC materials for its indoor applications. Ultimately, the perspective on this topic is presented after summing up the current progress of the research. Graphic abstract

Localized tissue bioimpedance is being widely investigated as a technique to identify physiological features in support of health focused applications. In support of this method being translated into wearable systems for continuous monitoring, it is critical to not only collect measurements but also evaluate their quality. This is necessary to reduce errors in equipment or measurement conditions from contributing data artifacts to datasets that will be analyzed. Two methods for artifact identification in resistance measurements of bioimpedance datasets are presented. These methods, based on thresholding and trend detection, are applied to localized knee bioimpedance datasets collected from two knee sites over 7 consecutive days in free-living conditions. Threshold artifacts were identified in 0.04% (longitudinal and transverse) and 0.69% (longitudinal)/3.50% (transverse) of the total data collected.

A study is performed to evaluate the performance of wearable Wireless Power Transfer (WPT) systems on various parts of the human body. Specifically, the effects of the human body on the Power Transfer Efficiency (PTE) of a Conformal Strongly Coupled Magnetic Resonance (CSCMR) WPT system is systematically examined. Two CSCMR systems are designed to operate at the 27.12 MHz Industrial, Scientific, and Medical (ISM) band, one on a 1.5 mm thick FR-4 substrate and one on a 1.5 mm thick ferrite sheet. Simulations and measurements of these two configurations are performed for 26 different placement locations on the human body. Considering our measured data on these 26 locations, it is found that when a CSCMR WPT system with a traditional substrate, such as FR-4, is placed directly on the skin of the human body, its PTE decreases on average by 7.2%. However, when the same system uses a ferromagnetic substrate instead of FR-4, its PTE decreases on average by only 1.6%. Additionally, a study of the Specific Absorption Rate (SAR) for the different placement locations is performed.

In this study, a wearable electromyogram (EMG) system on the forearm was designed to analyze finger movements for use in human-machine interfaces. The designed system measures the EMG signals without restricting the user's movements, analyzes these measurements through the software embedded in the system, and transmits the generated response to the output units to be controlled in real-time with wireless communication techniques. In the study, a three-channel EMG amplifier was designed and a system in which the NodeMCU V3 development board could be integrated was realized.With the system, the features of finger movements were obtained using the Mean Absolute Value (MAV) and classified using Support Vector Machines (SVM) and Random Forest (RF) methods. In offline tests, 99.47% accursacy with RF and 98.2% accuracy with SVM were obtained. The RF algorithm with 99.47% accuracy in offline tests was selected and integrated into the embedded system for online tests. In the online tests performed with five volunteers, the system was able to analyze finger movements with an average accuracy of 92.16%, and the commands associated with the finger movements analyzed by the system were sent to the clients with the User Datagram Protocol (UDP), and the related movements were displayed on the output unit interface. The system can work in real-time with a delay of 90 ms and instantaneous movements can be seen visually on the designed output unit interface. This study is an important step in the detection of muscle diseases, the control of EMG-based wearable prosthetic systems, and the design of unmanned vehicles that can be controlled by finger movements., Bu çalışmada, insan-makine arayüzlerinde kullanılmak amacıyla, parmak hareketlerinin çözümlenebilmesine yönelik önkol üzerine giyilebilir bir elektromiyogram (EMG) sistemi tasarlanmıştır. Tasarlanan sistem, kullanıcının hareketlerini kısıtlamadan EMG sinyallerinin ölçümünü yaparak bu ölçümleri sisteme gömülü yazılım aracılığıyla çözümlemekte, oluşturulan cevabı, kontrol edilecek çıkış birimlerine kablosuz iletişim teknikleri ile gerçek zamanlı olarak iletebilmektedir. Çalışmada, üç kanal yapısındaki EMG yükseltecin tasarımı yapılmış ve NodeMCU V3 geliştirme kartının entegre edilebileceği bir sistem gerçekleştirilmiştir. Tasarlanan sistem ile parmak hareketlerine ait öznitelikler mutlak ortalama değer (MOD) kullanılarak elde edilmiş; Destek Vektör Makineleri (DVM) ve Rastgele Orman (RO) yöntemleri kullanılarak sınıflandırılmıştır. Offline testlerde, RO ile %99.47, DVM ile %98.2 doğruluk oranları elde edilmiştir. Offline testlerde %99.47 doğruluk gösteren RO algoritması seçilerek, online testler için gömülü sisteme entegre edilmiştir. Sistem 5 gönüllü ile gerçekleştirilen online testlerde parmak hareketlerini ortalama %92.16 doğrulukla çözümleyebilmiş, sistemin çözümlediği parmak hareketleri ile ilişkilendirilen komutların Kullanıcı Veribloğu İletişim Kuralları (UDP) ağ protokolü ile istemcilere gönderilerek ilgili hareketlerin çıkış birimi arayüzünde görüntülenmesi sağlanmıştır. Sistem 90 ms sürelik bir gecikme ile gerçek zamanlı olarak çalışabilmekte ve tasarlanan çıkış birimi arayüzünde anlık olarak yapılan hareketler görsel olarak görülebilmektedir. Yapılan bu çalışma kas hastalıklarının tespiti, EMG tabanlı giyilebilir protez sistemlerin kontrolü, parmak hareketleri ile kontrol edilebilecek insansız araçların tasarımında önemli bir aşamadır.

In this paper, the adoption of Machine Learning (ML) classifiers is addressed to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces (BCIs). The proposed BCI is based on the classification of Steady-State Visually Evoked Potentials (SSVEPs). In this setup, Augmented Reality Smart Glasses are used to generate and display the flickering stimuli for the SSVEP elicitation. An experimental campaign was conducted on 20 adult volunteers. Successively, a Leave-One-Subject-Out Cross Validation was performed to validate the proposed algorithm. The obtained experimental results demonstrate that suitable ML-based processing strategies outperform the state-of-the-art techniques in terms of classification accuracy. Furthermore, it was also shown that the adoption of an inter-subjective model successfully led to a decrease in the 3-σ uncertainty: this can facilitate future developments of ready-to-use systems.

Stretchable conductors are important ingredients in the new emerging wearable systems. Here, intrinsically stretchable con­ductors (ISCs) based on carbon nanomaterials with a good balance of proper...

In order to document arrhythmias, indicated due to symptoms or for prognostic purposes, both invasive and noninvasive possibilities for ECG monitoring are available. The choice of the device for monitoring depends mainly on the frequency of arrhythmias. If they occur less than once a month, long-term monitoring becomes necessary which either continuously monitors the rhythm by an implantable device (implantables) or by wearable systems (wearables) which usually register the ECG discontinuously. Because wearables, e.g. smartphones, are basically ubiquitously available, they may be used for ECG monitoring. This paper comments on the use of implantables and wearables for the detection of atrial fibrillation and the documentation of symptomatic arrhythmias in syncope or palpitations.

The strain-gauge type textile sensors adopted in many studies on respiration-sensing wearable systems have been reported to have two major limitations that result in reduced sensing accuracy and insufficient durability of the sensor. The two limitations are the inability to accurately monitor the changes in the three-dimensional (3D) body contour during changes in the respiration cycle and the frequent occurrence of baseline drifts. To solve these issues, this study proposes new types of textile respiration rate sensors with a 3D structure, which measure the respiration rate based on the variation in the size of the contacting section’s surface during respiration, rather than the changes in the length of the sensor, as in existing strain-gauge type sensors. Firstly, the sensing signals were analyzed based on morphology and size measurements. Then, the sensing reliability of three respiration rate sensor types, namely the no-filler, 3D hard, and 3D soft types, was analyzed by comparing their measurements with those of the SS5LB. Finally, the reproducibility and baseline drifts of the sensors’ measurements were evaluated by taking and comparing repeated measurements. As a result, the consistency of the sensing signals of the SS5LB and those of the two types of 3D sensors was higher than those of the no-filler type sensor, and the 3D soft type sensor had the highest reliability and reproducibility among the three new types of sensors. The result showed relatively reduced baseline drifts in the two types of 3D sensors.

Wearable devices can be used to detect activities. The size of the wearable device is relativelysmall and easy to wear on clothing so it does not complicate the movement of its users. In thisstudy wearable devices or wearable systems were developed using a three-axis accelerometersensor. The detected activities are walking, sitting and lying down. The technique used todetect activity is to use a threshold value. For walking activities use the y axis accelerationwhose value has been reduced from the influence of gravity. The threshold value for thecurrent activity consists of two, namely: the lower threshold value and the upper thresholdvalue, the lower threshold value is between -1 to -2 and for the upper threshold value rangesfrom 1 to 2. The sitting and lying activity uses the value acceleration on the x, y, and z axes.For sitting activity the x-axis threshold value is between -0.5 to 0.5, the y-axis value is between9 and 10, and the z-axis value is -0.5 to 0.5. The threshold value of lying activities on the xaxis is between -0.5 and 0.5, the y-axis is between -0.5 and 0.5, while the z-axis is between 9and 10. The test results on wearable devices used to detect walking, sitting and lying activitiesshow, the percentage of results of running activities by 60%, for testing sitting activity by 65,while for lying down activity by 60%, each test is carried out 20 times.

Smart fabrics, generally regarded as smart Textiles are fabrics that have embedded electronics and interconnections woven into them, resulting in physical flexibility that is not achievable with other known electronic manufacturing techniques. Interconnections and components are intrinsic to the fabric therefore are not visible and less susceptible of getting tangled by surrounding objects. Smart fabrics can also more easily adapt to quick changes in the sensing and computational requirements of any specific application, this feature being useful for power management and context awareness. For electronic systems to be part of our day-to-day outfits such electronic devices need to conform to requirements as regards wear-ability, this is the vision of wearable technology. Wearable systems are characterized by their capability to automatically identify the activity and the behavioral status of their wearer as well as of the situation around them, and to use this information to adjust the systems' configuration and functionality. This write-up focused on recent developments in the field of Smart Fabrics and pays particular attention to the materials and their manufacturing techniques.

Wearable electronic systems are able to monitor and measure multiple biophysical, biochemical signals to help researchers develop further understandings of human health and correlation between human performance and diseases. Driven by increasing demand for need in sports training, health monitoring and disease diagnose, bio-integrated systems are developing at a significant speed based on recent advances in material science, structure design and chemical techniques. A wide range of wearable systems are created and feature unique measuring targets, methods and soft, transparent, stretchable characters. This review summarizes the recent advances in wearable electronic technologies that also include material science, chemical science and electronic engineering. The introduction to basic wearable fundamentals covers subsequent consideration for materials, system integration and promising platforms. Detailed classification towards their functions of physical, chemical detection is also mentioned. Strategies to achieve stretchability and promising material, AgNW, are fully discussed. This paper concludes with consideration of main challenging obstacles in this emerging filed and promises in materials that possess excellent potentials for predicted progress.

This work addresses an innovative processing strategy to improve the classification of Steady-State Visually Evoked Potentials (SSVEPs). This strategy resorts to the combined use of fast Fourier transform and Canonical Correlation Analysis in time domain, and manages to outperform by over 5% previous results obtained for highly wearable, single-channel Brain–Computer Interfaces. In fact, a classification accuracy of 90% is reached with only 2-s time response. Then, the proposed algorithm is employed for an experimental characterization of three different Augmented Reality (AR) devices (namely, Microsoft Hololens I, Epson Moverio BT-350, and Oculus Rift S). These devices are used to generate the flickering stimuli necessary to the SSVEP induction. Also, in the three pieces of instrumentation under test, the number of simultaneous visual stimuli was increased with respect to the state-of-art solutions. The aim of the experimental characterization was to evaluate the influence of different AR technologies on the elicitation of user's SSVEPs. Classification accuracy, time response, and information transfer rate were used as figures of merit on nine volunteers for each piece of instrumentation. Experimental results show that choosing an adequate AR headset is crucial for obtaining satisfying performance: in fact, it can be observed that the classification accuracy obtained with Microsoft Hololens is about 20% greater than Epson Moverio one.

International audience; This study presents a low-power wearable system able to predict a fall by detecting a pre-impact condition, performed through a simple analysis of motion data (acceleration) and height of the subject. The system can detect a fall in all directions with an average consumption of 5.91 mA; i.e., it can monitor the activity of daily living (ADL), whether or not a fall occurs. The entire detection system uses a single wearable tri-axis accelerometer placed on the waist for the comfort of the wearer during a long-term application. The algorithm is based on the following hypothesis: "A region defined as balanced boundary circle, based on the user's height, is characterized by the fact the chance that an actual fall happening is minimal. When an activity is classified outside this circle, an acceleration analysis is performed to determine an impending fall condition". Our threshold-based algorithm was validated experimentally, first with 9 young healthy volunteers performing both normal ADL and fall activities and then using 10 ADL and 5 falls from public SisFall dataset. The results show that falls could be detected with an average leadtime of 259 ms before the impact occurs, with minimal false alarms (97.7% specificity) and a sensitivity of 92.6%. This is a good lead-time achieved thus far in pre-impact fall detection, permitting the integration of our detection system in a wearable inflatable airbag for hip protection.

The continuous monitoring of an individual's breathing can be an instrument for the assessment and enhancement of human wellness. Specific respiratory features are unique markers of the deterioration of a health condition, the onset of a disease, fatigue and stressful circumstances. The early and reliable prediction of high-risk situations can result in the implementation of appropriate intervention strategies that might be lifesaving. Hence, smart wearables for the monitoring of continuous breathing have recently been attracting the interest of many researchers and companies. However, most of the existing approaches do not provide comprehensive respiratory information. For this reason, a meta-learning algorithm based on LSTM neural networks for inferring the respiratory flow from a wearable system embedding FBG sensors and inertial units is herein proposed. Different conventional machine learning approaches were implemented as well to ultimately compare the results. The meta-learning algorithm turned out to be the most accurate in predicting respiratory flow when new subjects are considered. Furthermore, the LSTM model memory capability has been proven to be advantageous for capturing relevant aspects of the breathing pattern. The algorithms were tested under different conditions, both static and dynamic, and with more unobtrusive device configurations. The meta-learning results demonstrated that a short one-time calibration may provide subject-specific models which predict the respiratory flow with high accuracy, even when the number of sensors is reduced. Flow RMS errors on the test set ranged from 22.03 L/min, when the minimum number of sensors was considered, to 9.97 L/min for the complete setting (target flow range: 69.231 ± 21.477 L/min). The correlation coefficient r between the target and the predicted flow changed accordingly, being higher (r = 0.9) for the most comprehensive and heterogeneous wearable device configuration. Similar results were achieved even with simpler settings which included the thoracic sensors (r ranging from 0.84 to 0.88; test flow RMSE = 10.99 L/min, when exclusively using the thoracic FBGs). The further estimation of respiratory parameters, i.e., rate and volume, with low errors across different breathing behaviors and postures proved the potential of such approach. These findings lay the foundation for the implementation of reliable custom solutions and more sophisticated artificial intelligence-based algorithms for daily life health-related applications.

This paper proposes the adoption of an innovative algorithm to enhance the performance of highly wearable, reactive Brain-Computer Interfaces (BCIs), which exploit the Steady-State Visually Evoked Potential (SSVEP) paradigm. In particular, a combined time-domain/frequency-domain processing is performed in order to reduce the number of features of the brain signals acquired. Successively, these features are classified by means of an Artificial Neural Network (ANN) with a learnable activation function. In this way, the user intention can be translated into commands for external devices. The proposed algorithm was initially tested on a benchmark data set, composed by 35 subjects and 40 simultaneous flickering stimuli, obtaining performance comparable with the state of the art. Successively, the algorithm was also applied to a data set realized with highly wearable BCI equipment. In particular, (i) Augmented Reality (AR) smart glasses were used to generate the flickering stimuli necessary to the SSVEPs elicitation, and (ii) a single-channel EEG acquisition was conducted for each volunteer. The obtained results showed that the proposed strategy provides a significant enhancement in SSVEPs classification with respect to other state-of-the-art algorithms. This can contribute to improve reliability and usability of brain computer interfaces, thus favoring the adoption of this technology also in daily-life applications.

In search and rescue missions, drone operations are challenging and cognitively demanding. High levels of cognitive workload can affect rescuers' performance, leading to failure with catastrophic outcomes. To face this problem, we propose a machine learning algorithm for real-time cognitive workload monitoring to understand if a search and rescue operator has to be replaced or if more resources are required. Our multimodal cognitive workload monitoring model combines the information of 25 features extracted from physiological signals, such as respiration, electrocardiogram, photoplethysmogram, and skin temperature, acquired in a noninvasive way. To reduce both subject and day inter-variability of the signals, we explore different feature normalization techniques, and introduce a novel weighted-learning method based on support vector machines suitable for subject-specific optimizations. On an unseen test set acquired from 34 volunteers, our proposed subject-specific model is able to distinguish between low and high cognitive workloads with an average accuracy of 87.3% and 91.2% while controlling a drone simulator using both a traditional controller and a new-generation controller, respectively.

Background Falls in senior people have high incidence& lead to severe injuries [1]. Application of smart wearable systems (with sensors to monitor user’s balance and corresponding instant reminder to let tusers adjust posture/motion) can effectively improve static standing balance [2], reduce reaction time and body sway in response to balance perturbation [3], improve walking pattern [4], and reduce the risk of falls [5, 6]. However, previous systems have not considered the daily monitor of user’s balance and falling risks, and the personalized reminder. Artificial intelligence (AI) and big data analytics have been widely used to monitor the daily physical activity [7], while few studies have utilized them to improve balance/gait and prevent falls. Methods This study has optimized previous devices by integrating AI technology and developed a new smart insole system. The system consisted of insoles with embedded sensors that can capture the foot motion and plantar pressure, smart watch that connected with insoles wirelessly and then transmitted the foot motion and force data to Cloud server via Wi-Fi, central Cloud server for big data transmission and storage, workstation for big data analytics and machine learning, and user interface for data visualization (e.g. smartphone, tablet, and/or laptop). Results & Discussion The system transmission rate was up to 30 Hz. The collected big data contained all sensor signals captured before and after delivering reminder, and from day-to-day monitoring of users. The customized reminder varied in the type, frequency, magnitude, and amount/dosage. This AI smart insole system enabled the monitor of daily balance and falling risks and the provision of timely-updated and customized reminder to users, which could potentially reduce the risk of falls and slips. It can also act as a balance-training device. References 1. Rubenstein.Age ageing, 2006. 35(suppl2):p.ii37-ii41. 2. Ma.Sensors, 2015. 15(12):p.31709-31722. 3. Ma&Lee.Human Movement Science, 2017. 55:p.54-60. 4. Ma.Topics in stroke rehabilitation, 2018. 25(1):p.20-27. 5. Wan.Archives of physical medicine& rehabilitation, 2016. 97(7):p.1210-1213. 6. Ma.Sensors, 2016. 16(4):p.434. 7. Badawi.Future Generation Computer Systems, 2017. 66:p.59-70.

In the year 2021, wearable technology could look beautiful and feel magical, but instead is exemplified by a plain wristband that looks suspiciously like a prison monitor. How can we make wearable technology that respects our privacy, enhances our daily lives, integrates with our other connected devices without leashing us to a smartphone, and visually expresses who we are? This study uses a novel method of participatory design fiction (PDFi) to understand potential users of everyday wearable technology through storytelling. I recruited participants from the general public and gave them a five-point prompt to create a design fiction (DF), which inspired the user-centred design of an everyday connected wearable device. The participants each received a technology probe to wear in the wild for a year. They then updated their DFs as a way to reflect on the implications of the technology. For the purposes of privacy, augmenting device functionality through interoperability, and integration into an Internet of Things (IoT) ecosystem, I used the Hub-of-All-Things personal data store to provide the software infrastructure. By listening to their stories, we can elicit design concepts directly from the users, to help us create wearable IoT devices that put the wearer at the centre of the design process, and are satisfying both functionally and emotionally., The Alan Turing Institute Doctoral Scheme, University of Cambridge Department of Computer Science and Technology, The Kenneth Hayter Memorial Fund

Recovering walking ability is one of the main goals of patients having suffered a stroke or a spinal cord lesion. Lower extremity robotic exoskeletons and neuroprosthesis are intended to compensate walking deficits or to provide gait training following lower limb paralysis. Prior to the design of exoskeletons and neuroprosthesis, the designers have to be informed with an application scenario in which how the device is going to assist walking is described. To date, the scenarios have been described as pathology-dependent (i.e. full assistance for complete paraplegia). However, most pathologies lead to similar functional impairments. One of the TAILOR project objectives is to understand the common functional features shared by the most prevalent pathologies, providing a single, common description of functional requirements to drive the design of lower limb robotic exoskeletons and neuroprosthesis. In this work we present three common scenarios for walking assistance for stroke and Spinal Cord Injury.

Inappropriate posture and the presence of spinal disorders require specific monitoring systems. In clinical settings, posture evaluation is commonly performed with visual observation, electrogoniometers or motion capture systems (MoCaps). Developing a measurement system that can be easily used also in non-structured environments would be highly beneficial for accurate posture monitoring. This work proposes a system based on three magneto-inertial measurement units (MIMU), placed on the backs of seventeen volunteers on the T3, T12 and S1 vertebrae. The reference system used for validation is a stereophotogrammetric motion capture system. The volunteers performed forward bending and sit-to-stand tests. The measured variables for identifying the posture were the kyphosis and the lordosis angles, as well as the range of movement (ROM) of the body segments. The comparison between MIMU and MoCap provided a maximum RMSE of 5.6° for the kyphosis and the lordosis angles. The average lumbo-pelvic contribution during forward bending (41.8 ± 8.6%) and the average lumbar ROM during sit-to-stand (31.8 ± 9.8° for sitting down, 29.6 ± 7.6° for standing up) obtained with the MIMU system agree with the literature. In conclusion, the MIMU system, which is wearable, inexpensive and easy to set up in non-structured environments, has been demonstrated to be effective in posture evaluation.

Increasing the accessibility of collaborative robotics requires interfaces that support intuitive teleoperation. One possibility for an intuitive interface is offered by wearable systems that measure the operator’s movement and use the information for robot control. Such wearable systems should preserve the operator’s movement capabilities and, thus, their ability to flexibly operate in the workspace. This paper presents a novel wireless wearable system that uses only inertial measurement units (IMUs) to determine the orientation of the operator’s upper body parts. An algorithm was developed to transform the measured orientations to movement commands for an industrial collaborative robot. The algorithm includes a calibration procedure, which aligns the coordinate systems of all IMUs, the operator, and the robot, and the transformation of the operator’s relative hand motions to the movement of the robot’s end effector, which takes into account the operator’s orientation relative to the robot. The developed system is demonstrated with an example of an industrial application in which a workpiece needs to be inserted into a fixture. The robot’s motion is compared between the developed system and a standard robot controller. The results confirm that the developed system is intuitive, allows for flexible control, and is robust enough for use in industrial collaborative robotic applications.

Wearable physiological monitoring systems are becoming increasingly prevalent in the push toward autonomous health monitoring and offer new modalities for playful and purposeful interaction within human computer interaction (HCI). Sensing systems that can be integrated into garments and, therefore, daily activities offer promising pathways toward ubiquitous integration. The electrocardiogram (ECG) signal is commonly monitored in healthcare and is increasingly utilized as a method of determining emotional and psychological state, however, the complete ECG waveform with the P, Q, R, S, and T peaks is not commonly used, due to the challenges associated with collecting the full waveform with wearable systems. We present woven textile electrodes as an option for garment-integrated ECG monitoring systems that are capable of capturing the complete ECG waveform. In this work, we present the changes in the peak detection performance caused by different sizes, patterns, and thread types with data from 10 human participants. These testing results provide empirically-derived guidelines for future woven textile electrodes, present a path forward for assessing design decisions, and highlight the importance of testing novel wearable sensor systems with more than a single individual.

Real-time “on-body” monitoring of human physiological signals through wearable systems developed on flexible substrates (e-skin) is the next target in human health control and prevention, while an alternative to bulky diagnostic devices routinely used in clinics. The present work summarizes the recent trends in the development of e-skin systems. Firstly, we revised the material development for e-skin systems. Secondly, aspects related to fabrication techniques were presented. Next, the main applications of e-skin systems in monitoring, such as temperature, pulse, and other bio-electric signals related to health status, were analyzed. Finally, aspects regarding the power supply and signal processing were discussed. The special features of e-skin as identified contribute clearly to the developing potential as in situ diagnostic tool for further implementation in clinical practice at patient personal levels.

Monitoring scapular movements is of relevance in the contexts of rehabilitation and clinical research. Among many technologies, wearable systems instrumented by strain sensors are emerging in these applications. An open challenge for the design of these systems is the optimal positioning of the sensing elements, since their response is related to the strain of the underlying substrates. This study aimed to provide a method to analyze the human skin strain of the scapular region. Experiments were conducted on five healthy volunteers to assess the skin strain during upper limb movements in the frontal, sagittal, and scapular planes at different degrees of elevation. A 6 × 5 grid of passive markers was placed posteriorly to cover the entire anatomic region of interest. Results showed that the maximum strain values, in percentage, were 28.26%, and 52.95%, 60.12% and 60.87%, 40.89%, and 48.20%, for elevation up to 90° and maximum elevation in the frontal, sagittal, and scapular planes, respectively. In all cases, the maximum extension is referred to the pair of markers placed horizontally near the axillary fold. Accordingly, this study suggests interesting insights for designing and positioning textile-based strain sensors in wearable systems for scapular movements monitoring.

International audience; Risk-taking behaviors of adult bedridden patients in neurosurgery are frequent, however little analyzed. We aimed to estimate from the literature and our clinical experience the incidence of the different clinical pictures. Risk-taking behaviors seem to be more frequent than reported. They are often minor, but they can lead to death, irrespective of the prescription of physical or chemical constraints. We also aimed to contextualize the risks, and to describe the means reducing the consequences for the patients. Two main conditions were identified, the loss of awareness of risk-taking behaviors by the patient, and uncontrolled body motions. Besides, current experience feedback analyses and new non-exclusive technological solutions could limit the complications, while improving prevention with wearable systems, neighborhood sensors, or room monitoring and service robots. Further research is mandatory to develop efficient and reliable systems avoiding complications and saving lives. Ethical and legal issues must also be accounted for, notably concerning the privacy of patients and caregivers.

Skin-interfaced wearable systems with integrated colorimetric assays, microfluidic channels, and electrochemical sensors offer powerful capabilities for noninvasive, real-time sweat analysis. This Perspective details recent progress in the development and translation of novel wearable sensors for personalized assessment of sweat dynamics and biomarkers, with precise sampling and real-time analysis. Sensor accuracy, system ruggedness, and large-scale deployment in remote environments represent key opportunity areas, enabling broad deployment in the context of field studies, clinical trials, and recent commercialization. On-body measurements in these contexts show good agreement compared to conventional laboratory-based sweat analysis approaches. These device demonstrations highlight the utility of biochemical sensing platforms for personalized assessment of performance, wellness, and health across a broad range of applications.

The technical transfer of plant movement principles offers vast potentials for the development of novel, autonomously actuated structures. In article number 2100411 by Tiffany Cheng, Achim Menges, and co‐workers introduces a design and material programming approach for 4D‐printing adaptive material systems through a case study of biomimetic design. Based on the force generating mechanism of the air potato (Dioscorea bulbifera), the authors demonstrate the manufacture of a self‐tightening orthotic device. [Image: see text]

Wearable electronics are becoming one of the key technologies in health care applications including health monitoring, data acquisitions, and real‐time diagnosis. The commercialization of next‐generation devices has been stymied by the lack of ultrathin, flexible, and reliable power sources. Wearable thermo‐electrochemical cells (TECs), which can convert body heat to electricity via an electrochemical process, are showing great promise as power sources for such wearable systems. TECs harvest orders of magnitude more voltage per temperature difference (Seebeck coefficient (1–34 mV K−1)) when compared to the more common thermoelectric generators (Seebeck coefficient ≈tens or hundreds of µV K−1). However, there still remain great challenges for TECs progressing towards wearable applications. This review summarizes the recent development of potentially wearable TECs with promise for body‐heat harvesting, with a specific focus on flexible electrode materials, solid‐state electrolytes, device fabrication, and strategies toward applications. It also clarifies the challenges and gives some future direction to enhance future investigations on high‐performance wearable TECs for practical and self‐powered wearable devices.

Sleep apnea is a sleep disorder that affects a large population. This disorder can cause or augment the exposure to cardiovascular dysfunction, stroke, diabetes, and poor productivity. The polysomnography (PSG) test, which is the gold standard for sleep apnea detection, is expensive, inconvenient, and unavailable to the population at large. This calls for more friendly and accessible solutions for diagnosing sleep apnea. In this paper, we examine how sleep apnea is detected clinically, and how a combination of advances in embedded systems and machine learning can help make its diagnosis easier, more affordable, and accessible. We present the relevance of machine learning in sleep apnea detection, and a study of the recent advances in the aforementioned area. The review covers research based on machine learning, deep learning, and sensor fusion, and focuses on the following facets of sleep apnea detection: (i) type of sensors used for data collection, (ii) feature engineering approaches applied on the data (iii) classifiers used for sleep apnea detection/classification. We also analyze the challenges in the design of sleep apnea detection systems, based on the literature survey.

Strain sensors based on conductive and flexible textiles are gaining great relevance in research applications, rehabilitation, and sports medicine for monitoring human joints movements, thanks to their flexibility, negligible weight, and easy integration into substrates and wearable systems. The purpose of this work was twofold: i) to carry out the static characterization and to analyze the hysteresis of two textile-based strain sensors integrated into a polymeric matrix, and ii) to evaluate the feasibility of these sensors in monitoring scapular movements during bilateral arms abduction-adduction and flexion-extension. Results showed a comparable range of resistance variation at 0-20% strain during static characterization, with a mean absolute gauge factor of 2.65 (sensor A) and 2.12 (sensor B). The maximum hysteresis error was always lower than 31% for sensor A and lower than 27% for sensor B. Both sensors showed promising results in monitoring scapular motion during the two selected single-plane upper limb movements.

Nowadays, there are several diseases which affect different systems of the body, producing changes in the correct functioning of the organism and the people lifestyles. One of them is Parkinson's disease (PD), which is defined as a neurodegenerative disorder provoked by the destruction of dopaminergic neurons in the brain, resulting in a set of motor and non-motor symptoms. As this disease affects principally to ancient people, several researchers have studied different treatments and therapies for stopping neurodegeneration and diminishing symptoms, to improve the quality patients' lives. The most common therapies created for PD are based on pharmacological treatment for controlling the degeneration advance and the physical ones which do not reveal the progress of patients. For this reason, this review paper opens the possibility for using wearable motion capture systems as an option for the control and study of PD. Therefore, it aims to (1) study the different wearable systems used for capture the movements of PD patients and (2) determine which of them bring better results for monitoring and assess PD people. For the analysis, it uses papers based on experiments that prove the functioning of several motion systems in different aspects as monitoring, treatment and diagnose of the disease. As a result, it works with 30 papers which describe the factors mentioned before. Additionally, the paper uses journals and literature review about the pathology, its characteristics and the function of wearable sensors for the correct understanding of the topic.

Although lung transplantation can be used to treat lung failure, limited donor organ availability creates a pressing need for improved artificial lung technology. This review discusses recent developments and future research pathways for respiratory assist devices and regenerative therapies intended to treat advanced lung disease. Hollow fiber membrane gas exchangers can be used to bridge lung failure patients to transplantation. Engineering improvements to such devices are on the verge of enabling longer term wearable systems that simplify and improve support. Progress with microchannel-based devices provides hope of smaller, more biomimetic devices that may even enable implantation; however, further development and testing are needed. Advances in cell-based technologies and tissue engineering have enabled early proof of concept of bioartificial lungs with properties similar to the native organ. Recent progress with artificial lungs has enabled better treatment as a bridge therapy. Continued advances in both engineering and biology will be necessary to achieve a truly implantable artificial lung capable of destination therapy.