Wearable sensing and feedback with applications in health and lifestyle

The maturity of wearable sensing, wireless technology, and data processing techniques enables the remote monitoring of physical activity and vital signs to promote health and well-being of individuals. Recently, much of research has focused on machine learning approaches to sensor data for delivering more intelligence into different health and fitness applications. However, there are different challenges associated with these approaches that limit us to reach an acceptable accuracy level. In this thesis, a comprehensive analysis of wearable accelerometer sensors in human activity recognition problem is conducted to address the classification issues while dealing with inter-person differences and data diversities. In addition, novel feature extraction and classification techniques are proposed to improve the accuracy as well as the worst-case sensitivity in the multi-class classification. The introduced techniques are experimentally validated by considering two state-of-the-art case studies. This work presents significant evidence that we can build accurate predictive models for sensor-based recognition and diagnostic problem under more realistic conditions. Furthermore, to reduce the computational costs of the decision-making process, an innovative algorithm is presented to analyze the variations in the periodic signals. It reduces the learning efforts by detecting any significant changes in the signals. There is also an increasing potential for integration of wearable sensors and haptic systems to improve human motion learning in a wide range of applications. Therefore, we investigate how real-time corrective feedback improves the user's performance in a health application. Finally, a 2D vibrotactile display is developed to transmit tactile stimuli onto the lower back of the users who can personalize the vibration variables. The customization capability of this system reduces the cognitive loadings for the users. This system can be beneficial and efficient not only for delivering complex feedback, but also for people with hearing and visual impairments.La maturité des senseurs portables, de la technologie sans fil, et des techniques de traitement des données permet de surveiller à distance l'activité physique et les signes vitaux pour promouvoir la sante et le bien-être des individus. Récemment, la plus grande partie de la recherche était concentrée sur les démarches de l'apprentissage par machine pour traiter les données des senseurs dans le domaine de la santé et l'application de conditionnement physique. Toutefois, il y a plusieurs défis qui sont associés avec ces approches et qui nous limitent à atteindre un niveau de précision acceptable. Dans cette thèse, une analyse compréhensive d'un accéléromètre portable dans le but de reconnaître l'activité humaine est faite pour adresser les problèmes de classification tout en considérant les différences d'interpersonnelles et la diversité des données. En plus, l'extraction originale des caractéristiques et les techniques de classification est proposée pour améliorer la précision et la sensibilité dans le pire des cas dans une classification multi-classe. Les techniques présentées sont validées expérimentalement en considérant deux études de cas dernier cri. Ceci est preuve du fait que nous pouvons construire des modèles prédictifs précis pour des problèmes reliés à la reconnaissance par les capteurs et le diagnostic de ceux-ci dans les conditions plus réalistes. De plus, pour réduire les coûts de calcul du processus de prise de décision, un algorithme innovant est présenté pour analyser les variations des signaux périodiques. L'algorithme réduit les efforts liés à l'apprentissage en détectant tout changement significatif des signaux. Il y a aussi un potentiel croissant pour l'intégration des senseurs portables et les systèmes haptiques pour améliorer l'apprentissage du mouvement humain dans un large éventail d'applications. Donc, nous enquêtons sur une rétroaction corrective en temps réel pour savoir comment celle-ci améliore la performance de l'utilisateur dans une application de sante. Enfin, un affichage 2D vibrotactile est développé pour transmettre des stimuli tactiles dans les lombes des utilisateurs qui peuvent personnaliser les variables de vibration. La capacité de personnalisation de ce système réduit les chargements cognitifs des utilisateurs. Ce système peut être bénéfique et efficace non seulement pour délivrer un retour d'information complexe, mais aussi pour les gens souffrant de troubles de vue et d'audition

A Medical Cloud-Based Platform for Respiration Rate Measurement and Hierarchical Classification of Breath Disorders

The measurement of human respiratory signals is crucial in cyberbiological systems. A disordered breathing pattern can be the first symptom of different physiological, mechanical, or psychological dysfunctions. Therefore, a real-time monitoring of the respiration patterns, as well as respiration rate is a critical need in medical applications. There are several methods for respiration rate measurement. However, despite their accuracy, these methods are expensive and could not be integrated in a body sensor network. In this work, we present a real-time cloud-based platform for both monitoring the respiration rate and breath pattern classification, remotely. The proposed system is designed particularly for patients with breathing problems (e.g., respiratory complications after surgery) or sleep disorders. Our system includes calibrated accelerometer sensor, Bluetooth Low Energy (BLE) and cloud-computing model. We also suggest a procedure to improve the accuracy of respiration rate for patients at rest positions. The overall error in the respiration rate calculation is obtained 0.53% considering SPR-BTA spirometer as the reference. Five types of respiration disorders, Bradapnea, Tachypnea, Cheyn-stokes, Kaussmal, and Biot’s breathing are classified based on hierarchical Support Vector Machine (SVM) with seven different features. We have evaluated the performance of the proposed classification while it is individualized to every subject (case 1) as well as considering all subjects (case 2). Since the selection of kernel function is a key factor to decide SVM’s performance, in this paper three different kernel functions are evaluated. The experiments are conducted with 11 subjects and the average accuracy of 94.52% for case 1 and the accuracy of 81.29% for case 2 are achieved based on Radial Basis Function (RBF). Finally, a performance evaluation has been done for normal and impaired subjects considering sensitivity, specificity and G-mean parameters of different kernel functions

A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results