Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Guidage non-intrusif d'un bras robotique à l'aide d'un bracelet myoélectrique à électrode sèche

Depuis plusieurs années la robotique est vue comme une solution clef pour améliorer la qualité de vie des personnes ayant subi une amputation. Pour créer de nouvelles prothèses intelligentes qui peuvent être facilement intégrées à la vie quotidienne et acceptée par ces personnes, celles-ci doivent être non-intrusives, fiables et peu coûteuses. L’électromyographie de surface fournit une interface intuitive et non intrusive basée sur l’activité musculaire de l’utilisateur permettant d’interagir avec des robots. Cependant, malgré des recherches approfondies dans le domaine de la classification des signaux sEMG, les classificateurs actuels manquent toujours de fiabilité, car ils ne sont pas robustes face au bruit à court terme (par exemple, petit déplacement des électrodes, fatigue musculaire) ou à long terme (par exemple, changement de la masse musculaire et des tissus adipeux) et requiert donc de recalibrer le classifieur de façon périodique. L’objectif de mon projet de recherche est de proposer une interface myoélectrique humain-robot basé sur des algorithmes d’apprentissage par transfert et d’adaptation de domaine afin d’augmenter la fiabilité du système à long-terme, tout en minimisant l’intrusivité (au niveau du temps de préparation) de ce genre de système. L’aspect non intrusif est obtenu en utilisant un bracelet à électrode sèche possédant dix canaux. Ce bracelet (3DC Armband) est de notre (Docteur Gabriel Gagnon-Turcotte, mes co-directeurs et moi-même) conception et a été réalisé durant mon doctorat. À l’heure d’écrire ces lignes, le 3DC Armband est le bracelet sans fil pour l’enregistrement de signaux sEMG le plus performant disponible. Contrairement aux dispositifs utilisant des électrodes à base de gel qui nécessitent un rasage de l’avant-bras, un nettoyage de la zone de placement et l’application d’un gel conducteur avant l’utilisation, le brassard du 3DC peut simplement être placé sur l’avant-bras sans aucune préparation. Cependant, cette facilité d’utilisation entraîne une diminution de la qualité de l’information du signal. Cette diminution provient du fait que les électrodes sèches obtiennent un signal plus bruité que celle à base de gel. En outre, des méthodes invasives peuvent réduire les déplacements d’électrodes lors de l’utilisation, contrairement au brassard. Pour remédier à cette dégradation de l’information, le projet de recherche s’appuiera sur l’apprentissage profond, et plus précisément sur les réseaux convolutionels. Le projet de recherche a été divisé en trois phases. La première porte sur la conception d’un classifieur permettant la reconnaissance de gestes de la main en temps réel. La deuxième porte sur l’implémentation d’un algorithme d’apprentissage par transfert afin de pouvoir profiter des données provenant d’autres personnes, permettant ainsi d’améliorer la classification des mouvements de la main pour un nouvel individu tout en diminuant le temps de préparation nécessaire pour utiliser le système. La troisième phase consiste en l’élaboration et l’implémentation des algorithmes d’adaptation de domaine et d’apprentissage faiblement supervisé afin de créer un classifieur qui soit robuste au changement à long terme.For several years, robotics has been seen as a key solution to improve the quality of life of people living with upper-limb disabilities. To create new, smart prostheses that can easily be integrated into everyday life, they must be non-intrusive, reliable and inexpensive. Surface electromyography provides an intuitive interface based on a user’s muscle activity to interact with robots. However, despite extensive research in the field of sEMG signal classification, current classifiers still lack reliability due to their lack of robustness to short-term (e.g. small electrode displacement, muscle fatigue) or long-term (e.g. change in muscle mass and adipose tissue) noise. In practice, this mean that to be useful, classifier needs to be periodically re-calibrated, a time consuming process. The goal of my research project is to proposes a human-robot myoelectric interface based on transfer learning and domain adaptation algorithms to increase the reliability of the system in the long term, while at the same time reducing the intrusiveness (in terms of hardware and preparation time) of this kind of systems. The non-intrusive aspect is achieved from a dry-electrode armband featuring ten channels. This armband, named the 3DC Armband is from our (Dr. Gabriel Gagnon-Turcotte, my co-directors and myself) conception and was realized during my doctorate. At the time of writing, the 3DC Armband offers the best performance for currently available dry-electrodes, surface electromyographic armbands. Unlike gel-based electrodes which require intrusive skin preparation (i.e. shaving, cleaning the skin and applying conductive gel), the 3DC Armband can simply be placed on the forearm without any preparation. However, this ease of use results in a decrease in the quality of information. This decrease is due to the fact that the signal recorded by dry electrodes is inherently noisier than gel-based ones. In addition, other systems use invasive methods (intramuscular electromyography) to capture a cleaner signal and reduce the source of noises (e.g. electrode shift). To remedy this degradation of information resulting from the non-intrusiveness of the armband, this research project will rely on deep learning, and more specifically on convolutional networks. The research project was divided into three phases. The first is the design of a classifier allowing the recognition of hand gestures in real-time. The second is the implementation of a transfer learning algorithm to take advantage of the data recorded across multiple users, thereby improving the system’s accuracy, while decreasing the time required to use the system. The third phase is the development and implementation of a domain adaptation and self-supervised learning to enhance the classifier’s robustness to long-term changes

Advanced Sensing and Image Processing Techniques for Healthcare Applications

This Special Issue aims to attract the latest research and findings in the design, development and experimentation of healthcare-related technologies. This includes, but is not limited to, using novel sensing, imaging, data processing, machine learning, and artificially intelligent devices and algorithms to assist/monitor the elderly, patients, and the disabled population

Advanced Information Systems and Technologies

This book comprises the proceedings of the V International Scientific Conference "Advanced Information Systems and Technologies, AIST-2017". The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing computer networking and telecomunications. They will be useful for students, graduate students, researchers who interested in computer science

Wearable Biosensors to Understand Construction Workers' Mental and Physical Stress

Occupational stress is defined as harmful physical and mental responses when job requirements are greater than a worker's capacity. Construction is one of the most stressful occupations because it involves physiologically and psychologically demanding tasks performed in a hazardous environment this stress can jeopardize construction safety, health, and productivity. Various instruments, such as surveys and interviews, have been used for measuring workers’ perceived mental and physical stress. However valuable, such instruments are limited by their invasiveness, which prevents them from being used for continuous stress monitoring. The recent advancement of wearable biosensors has opened a new door toward the non-invasive collection of a field worker’s physiological signals that can be used to assess their mental and physical status. Despite these advancements, challenges remain: acquiring physiological signals from wearable biosensors can be easily contaminated from diverse sources of signal noise. Further, the potential of these devices to assess field workers’ mental and physical status has not been examined in the naturalistic work environment. To address these issues, this research aims to propose and validate a comprehensive and efficient stress-measurement framework that recognizes workers mental and physical stress in a naturalistic environment. The focus of this research is on two wearable biosensors. First, a wearable EEG headset, which is a direct measurement of brain waves with the minimal time lag, but it is highly vulnerable to various artifacts. Second, a very convenient wristband-type biosensor, which may be used as a means for assessing both mental and physical stress, but there is a time lag between when subjects are exposed to stressors and when their physiological signals change. To achieve this goal, five interrelated and interdisciplinary studies were performed to; 1) acquire high-quality EEG signals from the job site; 2) assess construction workers’ emotion by measuring the valence and arousal level by analyzing the patterns of construction workers’ brainwaves; 3) recognize mental stress in the field based on brain activities by applying supervised-learning algorithms;4) recognize real-time mental stress by applying Online Multi-Task Learning (OMTL) algorithms; and 5) assess workers’ mental and physical stress using signals collected from a wristband biosensor. To examine the performance of the proposed framework, we collected physiological signals from 21 workers at five job sites. Results yielded a high of 80.13% mental stress-recognition accuracy using an EEG headset and 90.00% physical stress-recognition accuracy using a wristband sensor. These results are promising given that stress recognition with wired physiological devices within a controlled lab setting in the clinical domain has, at best, a similar level of accuracy. The proposed wearable biosensor-based, stress-recognition framework is expected to help us better understand workplace stressors and improve worker safety, health, and productivity through early detection and mitigation of stress at human-centered, smart and connected construction sites.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149965/1/hjebelli_1.pd

To do or not to do (exercise) : physical activity in the lives of people with cerebral palsy

We examined the lives of adults with cerebral palsy who had minimal involvement in physical activity (Judy, aged 60; Alana, aged 29), who were involved in physical activity (Amy, aged 25; Ben, aged, 30), or who had minimal involvement in physical activity and who then participated in physical activity (David, aged 27; Tim, aged, 24). After receiving ethical approval, a life-history research approach (Denzin, 1989: Interpretive biography. Newbury Park, CA: Sage) was used, with the participants&rsquo; stories being interpreted using primarily psychodynamic theory (Freud, Erikson, Adler, Basch) to gain insight into their meaning and experiences of physical activity. Judy and Alana had similar childhood experiences, which included: performing difficult, and sometimes painful, physiotherapy; wearing callipers to assist their walking; lacking competence at physical activity; and being socially isolated from their classmates. These aspects of their life histories seemed to contribute to their subsequent avoidance of physical activity and early onset of functional decline.Amy and Ben had negative experiences with physical activity as children (similar to Judy and Alana), but were involved in, and valued, physical activity as adults. Physical activity was a means of displaying competence, delaying further functional loss, and becoming socially connected.David and Tim lost the ability to walk in early adolescence. The minimal physical activity in which they engaged during their adult lives was directed towards trying to walk again. Walking seemed to be intimately connected with psychosocial growth. David&rsquo;s weight-training programme seemed to provide him with another avenue for self-improvement towards his goal of attracting a life partner. Tim&rsquo;s warm-water aerobic programme provided him with an opportunity to develop competence at swimming and at walking, and to enhance his self-esteem for these activities.Involvement in physical activity may be important for people with cerebral palsy in their endeavours to successfully face the various psychosocial challenges throughout life. Implications of this research include: parents and teachers of children with cerebral palsy should provide support for their involvement in physical activity; physiotherapists should try to reduce the pain and increase the perceived relevancy of the treatments they deliver to young people with cerebral palsy; and psychologists should be aware of some of the difficulties people with cerebral palsy face and how they may manifest in adults with the condition.<br /

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems