Emerging body-wearable sensor devices for continuous health monitoring are severely energy constrained and yet required to offer high communication reliability under fluctuating channel conditions. This thesis aims at investigating the opportunities and challenges in the use of dynamic radio transmit power control for prolonging the lifetime of such devices. We first present extensive empirical evidence that the wireless link quality can change rapidly in body area networks, and a fixed transmit power results in either wasted energy (when the link is good) or low reliability (when the link is bad). We then propose a class of schemes feasible for practical implementation that adapt transmit power in real-time based on feedback information from the receiver. We show conservative, balanced, and aggressive adaptations of our scheme that progressively achieve higher energy savings of 14%-30% in exchange for higher potential packet losses (up to 10%). We also provide guidelines on how the parameters can be tuned to achieve the desired trade-off between energy savings and reliability within the chosen operating environment. Finally, we implement and profile our scheme on a MicaZ mote based platform, demonstrating that energy savings are achievable even with imperfect feedback information, and report preliminary results on the ultra-low-power integrated healthcare monitoring platform from our collaborating partner Toumaz Technology. In conclusion, our work shows adaptive radio transmit power control as a low-cost way of extending the battery-life of severely energy constrained body wearable devices, and opens the door to further optimizations customized for specific deployment scenarios.

Emerging body-wearable sensor devices for continuous health monitoring are severely energy constrained and yet required to offer high communication reliability under fluctuating channel conditions. This thesis aims at investigating the opportunities and challenges in the use of dynamic radio transmit power control for prolonging the lifetime of such devices. We first present extensive empirical evidence that the wireless link quality can change rapidly in body area networks, and a fixed transmit power results in either wasted energy (when the link is good) or low reliability (when the link is bad). We then propose a class of schemes feasible for practical implementation that adapt transmit power in real-time based on feedback information from the receiver. We show conservative, balanced, and aggressive adaptations of our scheme that progressively achieve higher energy savings of 14%-30% in exchange for higher potential packet losses (up to 10%). We also provide guidelines on how the parameters can be tuned to achieve the desired trade-off between energy savings and reliability within the chosen operating environment. Finally, we implement and profile our scheme on a MicaZ mote based platform, demonstrating that energy savings are achievable even with imperfect feedback information, and report preliminary results on the ultra-low-power integrated healthcare monitoring platform from our collaborating partner Toumaz Technology. In conclusion, our work shows adaptive radio transmit power control as a low-cost way of extending the battery-life of severely energy constrained body wearable devices, and opens the door to further optimizations customized for specific deployment scenarios.

The emergence of the Internet of Things (IoT) brought about the widespread of Body Sensor Networks (BSN) that continuously monitor patients using a collection of tiny-powered and lightweight bio-sensors offering convenience to both physicians and patients in the modern health care environment. Unfortunately, the deployment of bio-sensors in public hacker-prone settings means that they are vulnerable to various security threats exposing the security and privacy of patient information. This thesis presents an authentication scheme for each of two applications of medical sensor networks. The first is an ECC based authentication scheme suitable for a hospital-like setting whereby the patient is hooked up to sensors connected to a medical device such as an ECG monitor while the doctor needs real-time access to continuous sensor readings. The second protocol is a Chebyshev chaotic map-based authentication scheme suitable for deployment on wearable sensors allowing readings from the lightweight sensors connected to patients to be sent and stored on a trusted server while the patient is on the move. We formally and informally proved the security of both schemes. We also simulated both of them on AVISPA to prove their resistance to active and passive attacks. Moreover, we analyzed their performance to show their competitiveness against similar schemes and their suitability for deployment in each of the intended scenarios.

Parkinson’s disease is associated with reduced coordination between respiration and locomotion. For the neurological rehabilitation research, it requires a long-time monitoring system, which enables the online analysis of the patient’s vegetative locomotor coordination. In this work a real time step detector using three-dimensional accelerometer signal for the patients with Parkinson‘s disease is developed. This step detector is a complement for a recently developed system included of intelligent, wirelessly communicating sensors. The system helps to focus on the scientific questions whether this coordination may serve as a measure for the rehabilitation progress of PD patients. / +46-762453110 +46-462886970

A realtime wireless motion tracking system is developed. The system is capable of tracking the orientations of multiple wireless sensors, using a semi-distributed implementation to reduce network bandwidth and latency, to produce real-time animation of rigid body models, such as the human skeleton. The system has been demonstrated to be capable of full-body posture tracking of a human subject using fifteen devices communicating with a basestation over a single, low bandwidth, radio channel. The thesis covers the theory, design, and implementation of the tracking platform, the evaluation of the platform’s performance, and presents a summary of possible future applications.

Le réseau de capteurs porté est une technologie d’avenir prometteuse à multiple domaines d’application allant du médical à l’interface homme machine. Le projet BoWI a pour ambition d’évaluer la possibilité d’élaborer un réseau de capteurs utilisable au quotidien dans un large spectre d’applications et ergonomiquement acceptable pour le grand public. Cela induit la nécessité de concevoir un nœud de réseau ultra basse consommation pour à la fois convenir à une utilisation prolongée et sans encombrement pour le porteur. La solution retenue est de concevoir un nœud capable de travailler avec une énergie comparable à ce que l’état de l’art de la récolte d’énergie est capable de fournir. Une solution ASIC est privilégiée afin de tenir les contraintes d’intégration et de basse consommation. La conception de l’architecture dédiée a nécessité une étude préalable à plusieurs niveaux. Celle-ci comprend un état de l’art de la récolte d’énergie afin de fixer un objectif de budget énergie/puissance de notre système. Une étude des usages du système a été nécessaire notamment pour la reconnaissance postures afin de déterminer les cas d’applications types. Cette étude a conduit au développement d’algorithmes permettant de répondre aux applications choisies tout en s’assurant de la viabilité de leurs implantations. Le budget énergie fixé est un objectif de 100µW. Les applications choisies sont la reconnaissance de posture, la reconnaissance de geste et la capture de mouvement. Les solutions algorithmiques choisis sont une fusion de données de capteurs inertiels par Filtre de Kalman étendu (EKF) et l’ajout d’une classification par analyse en composante principale. La solution retenue pour obtenir des résultats d’implémentation est la synthèse de haut niveau qui permet un développement rapide. Les résultats de l’implantation matérielle sont dominés principalement par l’EKF. À la suite de l’étude, il apparait qu’il est possible avec une technologie 28nm d’atteindre les objectifs de budget énergie pour la partie algorithme. Une évaluation de la gestion haut niveau de tous les composants du nœud est également effectuée afin de donner une estimation plus précise des performances du système dans un cas d’application réel. Une contribution supplémentaire est obtenue avec l’ajout de la détection d’activité qui permet de prédire la charge de calcul nécessaire et d’adapter dynamiquement l’utilisation des ressources de traitement et des capteurs afin d’optimiser l’énergie en fonction de l’activité / Wireless Body Sensor Network (WBSN) is a promising technology that can be used in a lot of application domains from health care to Human Machine Interface (HMI). The BoWI project ambition is to evaluate and design a WBSN that can be used in various applications with daily usage and accessible to the public. This necessitates to design a ultra-low power node that reach a day of use without discomfort for the user. The elected solution is to design a node that operates with the power budget similar to what can be provided by the state of the art of the energy harvesting. An Application Specific Integrated Circuit (ASIC) solution is privileged in order to meet the integration and low power constraints. Designing the dedicated architecture required a preliminary study at several level which are: a state of the art of the energy harvesting in order to determine the objective of energy/power budget of our system, A study of the usage of the system to determine and select typical application cases. A study of the algorithms to address the selected applications while considering the implementation viability of the solutions. The power budget objective is set to 100µW. The application selected are the posture recognition, the gesture recognition and the motion capture. The algorithmic solution proposed are a data-fusion based on an Extended Kalman FIlter (EKF) with the addition of a classification using Principal Component Analysis (PCA). The implementation tool used to design the architecture is an High Level Synthesis (HLS) solution. Implementation results mainly focus on the EKF since this is by far the most power consuming digital part of the system. Using a 28nm technology the power budget objective can be reached for the algorithmic part. A study of the top level management of all components of the node is done in order to estimate performances of the system in real application case. This is possible using an activity detection which dynamically estimates the computing load required and then save a maximum of energy while the node is still.

Radio frequency (RF) communication, although most popular, is unsuitable for environments involving aqueous and animal/plant tissue media, cluttered environments (e.g., small regions with many radios), applications requiring extremely low power consumption, etc. For such environments, magnetic induction (MI) communication appears to be a viable new technology. It has many desirable properties for propagation in challenging environments. In this thesis, we have experimentally explored the use of Magnetic Induction (MI) based communications for communication through the body. Such communication modalities are essential for wireless communication between implanted therapeutic devices. RF is known to work poorly in this environment due to primarily an ionized aqueous propagation media. We have built a custom experimental testbed using magnetic coils and performed simulations of intrabody propagation for MI based communication using the Sim4Life package. Ultrasound (US) communications have been explored extensively for intra-body environments, and we compare MI against US as well. We experimentally showed that ultrasonic coupling (USC) works better than magnetic resonance coupling (MRC) for transmission through the body at 8 MHz frequency, as USC generates more power than MRC. We have also experimentally compared MR coupling against other forms of intra-body communication, such as galvanic and capacitive. We have done a deep in-depth study of in/on body simulation. According to those studies, the simulations work quite well, and yield a percentage error in the power received for USC as 3-4 %, while for MRC, as 4-5 %. The orientation of USC and MRC sensors causes only 1-2 % error, which doesn't have much impact. / Computer and Information Science

Made available in DSpace on 2016-06-02T19:05:57Z (GMT). No. of bitstreams: 1 4452.pdf: 7806002 bytes, checksum: 03f3df43afda775ba6b11c313093d1d5 (MD5) Previous issue date: 2012-06-13 / Financiadora de Estudos e Projetos / The physical inactivity has been indicated by the World Health Organization (WHO) as one of the main risk factors for the incidence of Chronic Non-Communicable Diseases (CNCDs). Millions of deaths in the world are a result of these diseases, and this number has increased each year. In an attempt to change this scenario WHO has stimulated regular practice of physical activities, because they play an important role in preventing CNCDs. In Brazil, these activities are performed by health units which generate a large amount of data that need treatment. To deal with this problem we developed UCEMEPA, an environment that employs Ubiquitous Computing technologies and wireless communication networks, in order to monitor remotely and evaluate participants of physical activity groups in real-time. This environment automatically collects physiologic data, and provides indicators which will support and direct public policies for promoting physical activity. In this sense, UCEMEPA will contribute for the promotion of health and quality of life, and for the conduction of longitudinal studies aiming to establish correlations between the practice of physical activity and CNCDs prevention. / A inatividade física tem sido apontada pela Organização Mundial de Saúde (OMS) como um dos principais fatores de risco comportamentais responsáveis pela incidência de Doenças Crônicas Não Transmissíveis (DCNTs). Milhões de mortes no mundo são decorrentes dessas doenças e esse número vem aumentando a cada ano. Na tentativa de reverter esse quadro a OMS vem estimulando as práticas regulares de atividade física, já que estas possuem um importante papel na prevenção de DCNTs. No Brasil a promoção dessas atividades é realizada por unidades regionalizadas de saúde e geram uma grande quantidade de dados que carecem de processamento e tratamento. Em resposta a esse problema nós desenvolvemos o ACUMAAF, um ambiente que emprega tecnologias emergentes da Computação Ubíqua e redes de comunicação sem fio para monitorar e avaliar, em tempo real e a distância, participantes de grupos de atividade física. Esse ambiente coleta dados fisiológicos de forma automática e coletiva e tem como objetivo possibilitar a geração de indicadores capazes de apoiar e nortear políticas públicas de promoção de atividade física. O ACUMAAF é um ambiente computacional com contribuições para a promoção da saúde, para a promoção da qualidade de vida da população e para a realização de estudos longitudinais objetivando relacionar atividade física e a prevenção de DCNTs.

Global rise in life expectancy has resulted in an increased demand for affordable healthcare and monitoring services. The advent of miniature and low–power sensor technologies coupled with the emergence of the Internet–of–Things has paved the way towards affordable health monitoring tools in wearable platforms. However, ensuring power–efficient operation, data accuracy and user comfort are critical for such wearable systems. This thesis focuses on the development of accurate and computationally efficient algorithms and low–cost, unobtrusive devices with potential predictive capability for monitoring mobility and cardiac health in a wearable platform. A three–stage complementary filter–based approach is developed to realize a computationally efficient method to estimate sensor orientation in real–time. A gradient descent–based approach is used to estimate the gyroscope integration drift, which is subsequently subtracted from the integrated gyroscope data to get the sensor orientation. This predominantly gyroscope–based orientation estimation approach is least affected by external acceleration and magnetic disturbances. A two–stage complementary filter–based efficient sensor fusion algorithm is developed for real–time monitoring of lower–limb joints that estimates the IMU inclinations in the first stage and uses a gradient descent–based approach in the second stage to estimate the joint angles. The proposed method estimates joint angles primarily from the gyroscope measurements without incorporating the magnetic field measurement, rendering the estimated angles least affected by any external acceleration and insensitive to magnetic disturbances. An IMU–based simple, low–cost and computationally efficient gait–analyzer is developed to track the course of an individual's gait health in a continuous fashion. Continuous monitoring of gait patterns can potentially enable detecting musculoskeletal or neurodegenerative diseases at the early onset. The proposed gait analyzer identifies an anomalous gait with moderate to high accuracy by evaluating the gait features with respect to the baseline clusters corresponding to an individual’s healthy peer group. The adoption of a computationally efficient signal analysis technique renders the analyzer suitable for systems with limited processing capabilities. A flexible dry capacitive electrode and a wireless ECG monitoring system with automatic anomaly detection capability are developed. The flexible capacitive electrode reduces motion artifacts and enables sensing bio–potential over a dielectric material such as cotton cloth. The virtual ground of the electrode allows for obtaining single–lead ECG using two electrodes only. ECG measurements obtained over different types of textile materials and in presence of body movements show comparable performance to other reported ECG monitoring systems. An algorithm is developed separately as a potential extension of the software to realize automatic identification of Atrial Fibrillation from short single–lead ECGs. The association between human gait and cardiac activities is studied. The gait is measured using wearable IMUs and the cardiac activity is measured with a single–lead handheld ECG monitor. Some key cardiac parameters, such as heart rate and heart rate variability and physical parameters, such as age and BMI show good association with gait asymmetry and gait variation. These associations between gait and heart can be useful in realizing low–cost in–home personal monitoring tool for early detecting CVD–related changes in gait features before the CVD symptoms are manifested. / Thesis / Doctor of Philosophy (PhD) / Wearable health monitoring systems can be a viable solution to meet the increased demand for affordable healthcare and monitoring services. However, such systems need to be energy–efficient, accurate and ergonomic to enable long–term monitoring of health reliably while preserving user comfort. In this thesis, we develop efficient algorithms to obtain real–time estimates of on–body sensors' orientation, gait parameters such as stride length, and gait velocity and lower–limb joint angles. Furthermore, we develop a simple, low–cost and computationally efficient gait–analyzer using miniature and low–power inertial motion units to track the health of human gait in a continuous fashion. In addition, we design flexible, dry capacitive electrodes and use them to develop a portable single–lead electrocardiogram (ECG) device. The flexible design ensures better conformity of the electrode to the skin, resulting in better signal quality. The capacitive nature allows for obtaining ECG signals over insulating materials such as cloth, thereby potentially enabling a comfortable means of long–term cardiac health monitoring at home. Besides, we implement an automatic anomaly detection algorithm that detects Atrial Fibrillation with good accuracy from short single–lead ECGs. Finally, we investigate the association between gait and cardiac activities. We observe that some important cardiac signs, such as heart rate and heart rate variability and physical parameters, such as age and BMI show good association with gait asymmetry and gait variation.