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.

Thèse en cotutelle avec l'Université de Strasbourg. / Le vieillissement de la population nous amène à imaginer de nouvelles approches pour promouvoir la santé et le bien-être à un âge avancé. Les liens sociaux et la mobilité quotidienne jouent potentiellement un rôle important, mais ces dynamiques sont actuellement mal comprises. Cette thèse vise à explorer comment les réseaux sociaux des aînés s'intègrent dans leurs espaces d'activité, et comment les relations sociales, inscrites dans l'environnement spatial, sont associées au bien-être. La thèse explore les données de l’étude CURHA collectées au Québec au sein de la cohorte NuAge. La population d’étude comprend 183 aînés âgés de 79 ans et plus enquêtés en 2014 et 2015, habitant dans les régions de Montréal et Sherbrooke. Le premier volet de cette thèse est de nature méthodologique, et concerne le développement d’un module de questionnaire cartographique interactif permettant de recueillir à la fois des lieux d’activité réguliers et des relations sociales, ces deux niveaux d'information connectés en fonction des “personnes vues dans les lieux d’activités”. Ces données ont été représentées en réseaux bipartites, ce qui a permis de distinguer des structures en étoile formées d’un nœud central connecté à un ensemble de nœuds périphériques. Ces structures identifient d’une part un lieu où l’aîné voit plusieurs personnes, qui pour la plupart, ne sont rencontrées qu’en ce lieu, et d’autre part une personne centrale avec qui plusieurs activités sont réalisées, de manière relativement exclusive.  Le deuxième volet de la thèse porte sur les liens entre les relations sociales et le bien-être émotionnel. Les relations sociales peuvent être catégorisées en relations proches, personnelles, ou de camaraderie. Le nombre de camarades et la présence d’une relation proche sont positivement associés au niveau de bien-être émotionnel, tandis que le nombre de relations personnelles est associé négativement au bien-être émotionnel.  Le dernier volet décrit de manière plus détaillée la distribution des catégories de relations sociales au sein des structures en étoiles identifiées dans les réseaux bipartites. Les relations proches représentent la majorité des personnes qui sont rencontrées ou qui accompagnent les personnes âgées dans un grand nombre de lieux d’activités. La résidence primaire est un lieu de socialisation importante, tandis que les lieux non résidentiels semblent servir de cadre privilégié aux relations de groupes. Les relations personnelles sont plus fréquentes au domicile des participants que dans les autres lieux d'activités. Cette thèse montre que l'analyse de réseau offre un cadre utile pour étudier les relations sociales en rapport avec l'environnement géospatial. Elle met en lumière la concentration de la vie sociale des aînés dans un nombre restreint de lieux d’activités, ainsi que la distribution spatiale différentielle de catégories de relations sociales spécifiquement associée au bien-être émotionnel. Ces approches peuvent soutenir la compréhension des influences socio-environnementales et favoriser la santé et le bien-être des aînés. / The aging of the population leads us to imagine new approaches to promote health and well-being in old age. Social connections and daily mobility potentially play an important role, but these dynamics are currently poorly understood. This thesis aims to explore how older adults' social networks fit into their activity spaces, and how social relationships, embedded in the spatial environment, are associated with well-being. The thesis explores data from the Contrasted Urban settings for Healthy Aging (CURHA) study collected in Quebec within the NuAge cohort. The study population includes 183 older adults aged 79 years and older surveyed in 2014 and 2015, living in the Montréal and Sherbrooke regions. The first part of this thesis is methodological, and concerns the development of an interactive cartographic questionnaire module allowing to collect both regular activity locations and social relationships, those two layers of information connected by “people seen at activity locations”. These data were represented as bipartite networks, which allowed us to identify star-like structures formed by a central node connected to a set of peripheral nodes. These structures identify, on the one hand, a location where the older adult sees several people, most of whom he or she meets only in that place, and on the other hand, a central person with whom several activities are carried out, while being the only person seen in most of these locations. The second part of the thesis focuses on the links between social relationships and emotional well-being. Social relationships can be categorized into close relationships, personal relationships, or companionship. The number of companions and the presence of a close relationship are positively associated with emotional well-being, while the number of personal relationships is negatively associated with emotional well-being.  The final section describes in more detail the distribution of social relationship categories within the star-like structures identified within the bipartite networks. Close relations represent the majority of people who are met or accompany the elderly in a large number of activity locations. The primary residence is an important place of socialization, while non-residential places seem to serve as a privileged setting for group relations. Personal relationships are most common in participants' homes. This thesis shows that network analysis provides a useful framework for studying social relationships within the geospatial environment. It highlights the concentration of older adults' social lives in a limited number of activity locations, as well as the differential spatial distribution of social relationship categories specifically associated with emotional well-being. These approaches can help to understand the socio-environmental influences on older adults' health and well-being.