Although steady progress has been made towards the development of a wearable pulse oximeter to aid in remote physiological status monitoring (RPSM) and triage operations, the ability to extract accurate physiological data from a forehead pulse oximeter during extended periods of activity and in the presence of pressure disturbances acting on the sensor remains a significant challenge. This research was undertaken to assess whether the attachment method used to secure a pulse oximeter sensor affects arterial oxygen saturation (SpO2) and heart rate (HR) accuracy during motion. Additionally, two sensor housings were prototyped to assess whether isolating the sensor from external pressure disturbances could improve SpO2 and HR accuracy. The research revealed that measurement accuracy during walking is significantly affected by the choice of an attachment method. Specifically, the research indicated that an elastic band providing a contact pressure of 60 mmHg can result in decreased measurement error and improved reliability. Furthermore, the research validated that the two isolating housings we have investigated improve SpO2 and HR errors significantly at pressures as high as 1200 mmHg (160 kPa) compared to current commercial housings. This information may be helpful in the design of a more robust pulse oximeter sensor for use in RPSM.

O uso de tecnologias assistivas objetivando proporcionar melhor qualidade de vida a idosos está em franca ascensão. Uma das linhas de pesquisa nessa área é o uso de dispositivos para detecção de quedas de idosos, um problema cuja ocorrência é cada vez maior devido a diversos fatores, incluindo maior longevidade, maior número de pessoas vivendo sozinhas na velhice, entre outros. Este trabalho apresenta o desenvolvimento de um dispositivo vestível, um nó sensor de redes de sensores sem fio de ultra-baixo consumo. Também descreve a expansão de um sistema KNX, ao qual o dispositivo é integrado. O dispositivo é capaz de identificar quedas, auxiliando no monitoramento de idosos e, por sua vez, aumentando a segurança dos mesmos. O monitoramento é realizado através de acelerômetro e giroscópio de 3 eixos, acoplados ao peito do usuário, capaz de detectar quedas através de um algoritmo de análise de limites determinados a partir da fusão dos dados dos sensores. O sensor vestível utiliza tecnologia EnOcean, que propicia conexão sem fio com um sistema de automação de casas inteligentes, de acordo com a norma KNX, através da plataforma Home Assistant. Telegramas de alarmes são automaticamente enviados no caso de detecção de quedas, e acionam um atuador pertencente ao sistema KNX. Além de validar a tecnologia EnOcean para uso em dispositivos vestíveis, o protótipo desenvolvido não indicou nenhum falso positivo através de testes realizados com dois usuários de características corporais diferentes, onde foram reproduzidos 100 vezes cada um dos oito tipos de movimentos (quatro movimentos de quedas e quatro de não quedas). Os testes realizados com o dispositivo revelaram sensibilidade e de especificidade de até 96% e 100%, respectivamente. / The use of assistive technologies to provide quality of life for elderly is increasing. One of the research lines of this area is the use of devices for fall detection, which is an increasing problem due to many factors, including greater longevity, more elders living alone, among others. This work presents the development of a wearable device, a sensor node for ultra-low power networks. Also, describes the expansion of a KNX system, which the device is integrated. The device is able to detect falls which can aid the monitoring of the elderly people and improve security. The monitoring is done through a 3-axis accelerometer and gyroscope attached on the user’s chest. The fall detection is done by a threshold algorithm based on data fusion of the sensors. The wearable sensor is an EnOcean node, which includes a wireless connection with a smart home system, according to the KNX standard, through the Home Assistant platform. Alarm telegrams are automatically sent in case of fall detection, and fires an actuator that is part of the KNX system to alarm. In addition to validating the EnOcean’s Technology for use on wearable devices, the developed prototype didn’t indicated any false positives through tests performed with two users of different body characteristics, where each of the eight types of movements (four movements of falls and four of non-falls) were reproduced 100 times. The tests done with the device revealed sensitivity and specificity of up to 96% and 100%, respectively.

O uso de tecnologias assistivas objetivando proporcionar melhor qualidade de vida a idosos está em franca ascensão. Uma das linhas de pesquisa nessa área é o uso de dispositivos para detecção de quedas de idosos, um problema cuja ocorrência é cada vez maior devido a diversos fatores, incluindo maior longevidade, maior número de pessoas vivendo sozinhas na velhice, entre outros. Este trabalho apresenta o desenvolvimento de um dispositivo vestível, um nó sensor de redes de sensores sem fio de ultra-baixo consumo. Também descreve a expansão de um sistema KNX, ao qual o dispositivo é integrado. O dispositivo é capaz de identificar quedas, auxiliando no monitoramento de idosos e, por sua vez, aumentando a segurança dos mesmos. O monitoramento é realizado através de acelerômetro e giroscópio de 3 eixos, acoplados ao peito do usuário, capaz de detectar quedas através de um algoritmo de análise de limites determinados a partir da fusão dos dados dos sensores. O sensor vestível utiliza tecnologia EnOcean, que propicia conexão sem fio com um sistema de automação de casas inteligentes, de acordo com a norma KNX, através da plataforma Home Assistant. Telegramas de alarmes são automaticamente enviados no caso de detecção de quedas, e acionam um atuador pertencente ao sistema KNX. Além de validar a tecnologia EnOcean para uso em dispositivos vestíveis, o protótipo desenvolvido não indicou nenhum falso positivo através de testes realizados com dois usuários de características corporais diferentes, onde foram reproduzidos 100 vezes cada um dos oito tipos de movimentos (quatro movimentos de quedas e quatro de não quedas). Os testes realizados com o dispositivo revelaram sensibilidade e de especificidade de até 96% e 100%, respectivamente. / The use of assistive technologies to provide quality of life for elderly is increasing. One of the research lines of this area is the use of devices for fall detection, which is an increasing problem due to many factors, including greater longevity, more elders living alone, among others. This work presents the development of a wearable device, a sensor node for ultra-low power networks. Also, describes the expansion of a KNX system, which the device is integrated. The device is able to detect falls which can aid the monitoring of the elderly people and improve security. The monitoring is done through a 3-axis accelerometer and gyroscope attached on the user’s chest. The fall detection is done by a threshold algorithm based on data fusion of the sensors. The wearable sensor is an EnOcean node, which includes a wireless connection with a smart home system, according to the KNX standard, through the Home Assistant platform. Alarm telegrams are automatically sent in case of fall detection, and fires an actuator that is part of the KNX system to alarm. In addition to validating the EnOcean’s Technology for use on wearable devices, the developed prototype didn’t indicated any false positives through tests performed with two users of different body characteristics, where each of the eight types of movements (four movements of falls and four of non-falls) were reproduced 100 times. The tests done with the device revealed sensitivity and specificity of up to 96% and 100%, respectively.

O uso de tecnologias assistivas objetivando proporcionar melhor qualidade de vida a idosos está em franca ascensão. Uma das linhas de pesquisa nessa área é o uso de dispositivos para detecção de quedas de idosos, um problema cuja ocorrência é cada vez maior devido a diversos fatores, incluindo maior longevidade, maior número de pessoas vivendo sozinhas na velhice, entre outros. Este trabalho apresenta o desenvolvimento de um dispositivo vestível, um nó sensor de redes de sensores sem fio de ultra-baixo consumo. Também descreve a expansão de um sistema KNX, ao qual o dispositivo é integrado. O dispositivo é capaz de identificar quedas, auxiliando no monitoramento de idosos e, por sua vez, aumentando a segurança dos mesmos. O monitoramento é realizado através de acelerômetro e giroscópio de 3 eixos, acoplados ao peito do usuário, capaz de detectar quedas através de um algoritmo de análise de limites determinados a partir da fusão dos dados dos sensores. O sensor vestível utiliza tecnologia EnOcean, que propicia conexão sem fio com um sistema de automação de casas inteligentes, de acordo com a norma KNX, através da plataforma Home Assistant. Telegramas de alarmes são automaticamente enviados no caso de detecção de quedas, e acionam um atuador pertencente ao sistema KNX. Além de validar a tecnologia EnOcean para uso em dispositivos vestíveis, o protótipo desenvolvido não indicou nenhum falso positivo através de testes realizados com dois usuários de características corporais diferentes, onde foram reproduzidos 100 vezes cada um dos oito tipos de movimentos (quatro movimentos de quedas e quatro de não quedas). Os testes realizados com o dispositivo revelaram sensibilidade e de especificidade de até 96% e 100%, respectivamente. / The use of assistive technologies to provide quality of life for elderly is increasing. One of the research lines of this area is the use of devices for fall detection, which is an increasing problem due to many factors, including greater longevity, more elders living alone, among others. This work presents the development of a wearable device, a sensor node for ultra-low power networks. Also, describes the expansion of a KNX system, which the device is integrated. The device is able to detect falls which can aid the monitoring of the elderly people and improve security. The monitoring is done through a 3-axis accelerometer and gyroscope attached on the user’s chest. The fall detection is done by a threshold algorithm based on data fusion of the sensors. The wearable sensor is an EnOcean node, which includes a wireless connection with a smart home system, according to the KNX standard, through the Home Assistant platform. Alarm telegrams are automatically sent in case of fall detection, and fires an actuator that is part of the KNX system to alarm. In addition to validating the EnOcean’s Technology for use on wearable devices, the developed prototype didn’t indicated any false positives through tests performed with two users of different body characteristics, where each of the eight types of movements (four movements of falls and four of non-falls) were reproduced 100 times. The tests done with the device revealed sensitivity and specificity of up to 96% and 100%, respectively.

This master’s thesis deals with the explanation of the concept of smart wearables and the different application possibilities. This work also includes examples of finished demonstration devices in this category. Part of this work is devoted to guide the selection of appropriate components for the design of new equipment in the category of smart wearable. The result of this thesis is designed wearable and charging station. This device is able to read user input and display that information on a smart device running under Android system connected via Bluetooth technology.

Wearable sensors and systems have become increasingly popular for diverse applications. An emerging technology for physical activity assessment is Smart Textile Systems (STSs), comprised of sensitive/actuating fiber, yarn, or fabric that can sense an external stimulus. All required components of an STS (sensors, electronics, energy supply, etc.) can be conveniently embedded into a garment, providing a fully textile-based system. Thus, STSs have clear potential utility for measuring health-relevant aspects of human activity, and to do so passively and continuously in diverse environments. For these reasons, STSs have received increasing interest in recent studies. Despite this, however, limited evidence exists to support the implementation of STSs during diverse applications. Our long-term goal was to assess the feasibility and accuracy of using an STS to monitor human activities. Our immediate objective was to investigate the accuracy of an STS in three representative applications with respect to occupational scenarios, healthcare, and activities of daily living. A particular STS was examined, consisting of a smart socks (SSs), using textile pressure sensors, and smart undershirt (SUS), using textile strain sensors. We also explored the relative merits of these two approaches, separately and in combination. Thus, five studies were completed to design and evaluate the usability of the smart undershirt, and investigate the accuracy of implementing an STS in the noted applications. Input from the SUS led to planar angle estimations with errors on the order of 1.3 and 9.4 degrees for the low-back and shoulder, respectively. Overall, individuals preferred wearing a smart textile system over an IMU system and indicated the former as superior in several aspects of usability. In particular, the short-sleeved T-shirt was the most preferred garments for an STS. Results also indicated that the smart shirt and smart socks, both individually and in combination, could detect occupational tasks, abnormal and normal gaits, and activities of daily living with greater than 97% accuracy. Based on our findings, we hope to facilitate future work that more effectively quantifies sedentary periods that may be deleterious to human health, as well as detect activity types that may be help or hinder health and fitness. Such information may be of use to individuals and workers, healthcare providers, and ergonomists. More specifically, further analyses from this investigation could provide strategies for: (a) modifying a sedentary lifestyle or work scenario to a more active one, and (b) helping to more accurately identify occupational injury risk factors associated with human movement. / PHD / The use of interactive or “smart” textiles that have sensing material(s) incorporated into them supports an emerging technology for physical activity assessment called Smart Textile Systems (STSs). STSs are an increasingly useful technology for researchers, athletes, patients, and others. Our aims in the current study were the development and assessment of a new smart undershirt (SUS) that was designed to monitor low-back and shoulder motions, and to evaluate the preferred placement and usability of two STSs. We also assessed the accuracy of two smart garments, smart socks (SSs) and the SUS, both individually and in combination. Accuracy was evaluated in terms of the ability of these systems to distinguish between diverse simulated occupational tasks, normal and abnormal walking patterns, and several typical daily activities. Our investigation indicated that STSs could discriminate between different human activities common in three domains: occupational scenarios, healthcare, and activities of daily life. We also found that both smart garments (i.e., SSs and SUS) provided similar accuracy for activity classification, typically exceeding 97%, and thus there was no clear superiority between these two smart garments. We conclude that, overall, smart garments represent a promising area of research and a potential alternative for discriminating and monitoring a range of human activities. Use of this technology in the future may have positive implications for health promotion.

L’électrophysiologie est l’étude des signaux électriques et électrochimiques générés par certaines cellules spécifiques tout comme par des organes entiers. Elle donne aux médecins l’opportunité de suivre le fonctionnement d’un seul neurone mais aussi de l’intégralité du cerveau. L’enregistrement de ces activités est essentiel pour le diagnostic de pathologies aussi diverses que les arythmies cardiaques, l’épilepsie ou la dégénération musculaire. Dans cette thèse, nous étudions différents types d’électrodes cutanées à base de matériaux organiques, de leur conception à leur évaluation préclinique. Notre approche est basée sur l’utilisation du polymère conducteur PEDOT :PSS et de gels ioniques, qui réduisent l’impédance de l’interface électrode-peau. De plus, nos électrodes sont conçues avec différents substrats fins et souples, plastiques ou textiles. Ceci appelle de nouvelles techniques de fabrications adaptées à ces substrats et aux matériaux organiques. Les électrodes sont caractérisées puis testées sur des volontaires afin de démontrer leurs excellentes performances par rapport aux électrodes médicales usuelles. L’évaluation de leur capacité à réduire le bruit et de leur stabilité sur plusieurs jours est effectuée sur des signaux venant des activités musculaires, cardiaques et cérébrales. Nous présentons également une électrode microscopique dite « active », basée sur le transistor organique électrochimique. Celui-ci permet d’amplifier et de filtrer in situ le signal. Parce que nos électrodes organiques cutanées possèdent un important potentiel industriel et clinique, nous étudions maintenant leur intégration dans des dispositifs médicaux de pointe. / Electrophysiology is the study of electrical and electrochemical signals generated by specific cells or whole organs. It gives doctors the opportunity to track the physiological behavior of a single neuron, as well as the integral brain. The recording of these activities is essential to diagnose and better understand diseases like cardiac arrhythmias, epilepsy, muscular degeneration and many more. In this thesis, we study different types of cutaneous electrodes based on organic materials, from conception to pre-clinical evaluation. Our approach is based on the usage of PEDOT:PSS conducting polymer and ionic gels in order to reduce impedance at the skin-electrode interface. Moreover, the substrate of our electrodes is made with different materials such as thin and conformable plastics and textiles. Our devices are then flexible, motion resistant and can be integrating into clothes. We developed new fabrication processes, considering the different substrates and organic materials specifics. The electrodes were characterized and then tested on human volunteers to show their excellent performance in comparison to standard medical electrodes. The evaluation of noise reduction capabilities and possibilities to perform long-term recordings were established on signals coming from muscles, heart and brain. Furthermore, we present a hundred micrometer-small “active” electrode, based on the organic electrochemical transistor. It enables in situ amplification and filtering of recorded signals. The wearable organic electrodes developed in this work are of great industrial and clinic interest. Future work will aim to integrate these technologies into state-of-the-art medical devices.

The main vision of wearable computing is to make electronic systems an important part of everyday clothing in the future which will serve as intelligent personal assistants. Wearable devices have the potential to be wearable computers and not mere input/output devices for the human body. The present thesis focuses on introducing a new wearable computing paradigm, where the processing elements are closely coupled with the sensors that are distributed using Instruction Systolic Array (ISA) architecture. The thesis describes a novel, multiple sensor, multiple processor system architecture prototype based on the Instruction Systolic Array paradigm for distributed computing on fabrics. The thesis introduces new programming model to implement the distributed computer on fabrics. The implementation of the concept has been validated using parallel algorithms. A real-time shape sensing and reconstruction application has been implemented on this architecture and has demonstrated a physical design for a wearable system based on the ISA concept constructed from off-the-shelf microcontrollers and sensors. Results demonstrate that the real time application executes on the prototype ISA implementation thus confirming the viability of the proposed architecture for fabric-resident computing devices.

本研究では小型軽量な移動式床反力計と加速度センサ,ジャイロセンサおよび地磁気センサからなるウエアラブルな運動計測装置を開発し,本計測装置に適した動力学解析手法を提案して,動作解析システムとしての妥当性の検証を行なうことを目的とした.移動式床反力計の計測精度について定量的に評価し,本運動計測装置を用いて運動中の下肢の姿勢を算出した.さらに,本運動計測装置に適した動力学解析手法を提案し,提案手法にて算出された足関節モーメントの妥当性を検証した. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

Background: Parkinson disease (PD) is a neurodegenerative disorder with a variety of motor and nonmotor symptoms. Many of these symptoms can be monitored by eHealth solutions, including smartphone apps, wearable sensors, and camera systems. The usability of such systems is a key factor in long-term use, but not much is known about the predictors of successful use and preferable methods to assess usability in patients with PD. Objective: This study tested methods to assess usability and determined prerequisites for successful use in patients with PD. - Methods: We performed comprehensive usability assessments with 18 patients with PD using a mixed methods usability battery containing the System Usability Scale, a rater-based evaluation of device-specific tasks, and qualitative interviews. Each patient performed the usability battery with 2 of 3 randomly assigned devices: a tablet app, wearable sensors, and a camera system. The usability battery was administered at the beginning and at the end of a 4-day testing period. Between usability batteries, the systems were used by the patients during 3 sessions of motor assessments (wearable sensors and camera system) and at the movement disorder ward (tablet app). - Results: In this study, the rater-based evaluation of tasks discriminated the best between the 3 eHealth solutions, whereas subjective modalities such as the System Usability Scale were not able to distinguish between the systems. Successful use was associated with different clinical characteristics for each system: eHealth literacy and cognitive function predicted successful use of the tablet app, and bettermotor function and lower age correlated with the independent use of the camera system. The successful use of the wearable sensors was independent of clinical characteristics. Unfortunately, patients who were not able to use the devices well provided few improvement suggestions in qualitative interviews. Conclusions: eHealth solutions should be developed with a specific set of patients in mind and subsequently tested in this cohort. For a complete picture, usability assessments should include a rater-based evaluation of task performance, and there is a need to develop strategies to circumvent the underrepresentation of poorly performing patients in qualitative usability research.