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The mission of the research presented in this thesis is to give computers the power to sense and react to human activities. Without the ability to sense the surroundings and understand what humans are doing, computers will not be able to provide active, timely, appropriate, and considerate services to the humans. To accomplish this mission, the work stands on the shoulders of two giants: Machine learning and ubiquitous computing. Because of the ubiquity of sensor-enabled mobile and wearable devices, there has been an emerging opportunity to sense, learn, and infer human activities from the sensor data by leveraging state-of-the-art machine learning algorithms. While having shown promising results in human activity recognition, most existing approaches using supervised or semi-supervised learning have two fundamental problems. Firstly, most existing approaches require a large set of labeled sensor data for every target class, which requires a costly effort from human annotators. Secondly, an unseen new activity cannot be recognized if no training samples of that activity are available in the dataset. In light of these problems, a new approach in this area is proposed in our research. This thesis presents our novel approach to address the problem of human activity recognition when few or no training samples of the target activities are available. The main hypothesis is that the problem can be solved by the proposed NuActiv activity recognition framework, which consists of modeling the hierarchical and sequential structure of human activities, as well as bringing humans in the loop of model training. By injecting human knowledge about the hierarchical nature of human activities, a semantic attribute representation and a two-layer attribute-based learning approach are designed. To model the sequential structure, a probabilistic graphical model is further proposed to take into account the temporal dependency of activities and attributes. Finally, an active learning algorithm is developed to reinforce the recognition accuracy using minimal user feedback. The hypothesis and approaches presented in this thesis are validated by two case studies and real-world experiments on exercise activities and daily life activities. Experimental results show that the NuActiv framework can effectively recognize unseen new activities even without any training data, with up to 70-80% precision and recall rate. It also outperforms supervised learning with limited labeled data for the new classes. The results significantly advance the state of the art in human activity recognition, and represent a promising step towards bridging the gap between computers and humans.

Les technologies portables sont dans le processus de révolutionner le domaine de la santé en offrant des données vitales qui assistent dans la prévention et le traitement des maladies. Les appareils portables de la santé (HWDs), comme le vêtement biométrique de Hexoskin, sont à la pointe de cette innovation en offrant des données physiologiques détaillées et en ayant un impact significatif dans les domaines comme l’analyse de la démarche et la surveillance des activités. Le but de cette étude est de développer des modèles précis de machine learning et deep learning capables de prédire les activités humaines à l’aide de données provenant des technologies portables Hexoskin. Ceci implique l’analyse des données des capteurs comme la fréquence cardiaque et les mouvements du torse dans l’optique de prédire avec précision les activités telles que la marche, la course et le sommeil. Cette étude a fait l’objet d’une collecte de données des capteurs de 52 participants sur une période de deux semaines à l’aide des technologies portables Hexoskin. Plusieurs techniques avancées d’ingénierie des caractéristiques ont été appliquées pour extraire des caractéristiques critiques comme les accélérations X, Y et Z. Plusieurs algorithmes de machine learning tels que le Balanced Random Forest (BRF), XGradient Boosting et LSTM (sans ingénierie des caractéristiques) ont été utilisés pour l’analyse des données. Les modèles ont été entraînés et testés sur des données provenant d’Hexoskin pour évaluer leurs performances basées sur l’exactitude, le rappel, la précision et du score F1. Cette étude démontre que les technologies portables Hexoskin, couplées à des modèles de machine learning sophistiqués, pouvaient prédire avec une grande précision les activités humaines. La recherche valide l’efficacité des technologies portables Hexoskin dans la reconnaissance des activités humaines, en mettant en lumière leur potentielle utilisation dans le domaine de la santé, l’analyse de la démarche, et la surveillance des activités. Cette étude contribue de manière significative à l’amélioration des standards de soins médicaux et ouvre des nouvelles perspectives pour le diagnostic et le traitement des conditions liées à la démarche. L’intégration des technologies Hexoskin avec des algorithmes de machine learning représente un pas en avant significatif dans la surveillance continue et en temps réel des maladies chroniques, v positionnant ainsi Hexoskin comme un outil fiable pour une multitude d’applications dans le domaine de la santé. / Wearable technologies are revolutionizing the healthcare field by providing vital data that assists in the prevention and treatment of diseases. Health wearable devices (HWDs), like the Hexoskin biometric garment, are at the forefront of this innovation by offering detailed physiological data and significantly impacting fields such as gait analysis and activity monitoring. The aim of this study is to develop accurate machine learning and deep learning models capable of predicting human activities using data from Hexoskin wearable technologies. This involves analyzing sensor data such as heart rate and torso movements to accurately predict activities such as walking, running, and sleeping. This study involved collecting sensor data from 52 participants over a two-week period using Hexoskin wearable technologies. Several advanced feature engineering techniques were applied to extract critical features such as X, Y, and Z accelerations. Multiple machine learning algorithms, such as Balanced Random Forest (BRF), XGradient Boosting, and LSTM (without feature engineering), were used for data analysis. The models were trained and tested on data from Hexoskin to evaluate their performance based on accuracy, recall, precision, and F1 score. This study demonstrates that Hexoskin wearable technologies, coupled with sophisticated machine learning models, can predict human activities with high accuracy. The research validates the effectiveness of Hexoskin wearable technologies in human activity recognition, highlighting their potential use in healthcare, gait analysis, and activity monitoring. This study significantly contributes to improving medical care standards and opens new perspectives for the diagnosis and treatment of gait-related conditions. The integration of Hexoskin technologies with machine learning algorithms represents a significant step forward in the continuous and real-time monitoring of chronic diseases, positioning Hexoskin as a reliable tool for a multitude of applications in the healthcare field.

<p>There are no commercially available wearable shoe in-sole sensors that are capable of measuring and distinguishing between shear and compression forces. Companies have already developed shoe sensors that simply measure pressure and make general inferences on the collected data with elaborate software [2, 3, 4, 5]. Researchers have also attempted making sensors that are capable of measuring shear forces, but they are not well suited for biomedical applications [61, 62, 63, 64]. This work focuses on the development of a novel wearable sensor system that is capable of identifying and measuring shear and compression forces through the use of capacitive sensing. Custom hardware and software tools such as materials test systems and capacitive measurement systems were developed during this work. Numerous sensor prototypes were developed, characterized, and optimized during the scope of this project. Upon analysis of the data, the best capacitive measurement system developed in this work utilized the CAV444 IC chip, whereas the use of the Arduino-derived measurement system required data filtering using median and Butterworth zero phase low pass filters. The highest dielectric constant reported from optimization experiments yielded 9.7034 (+/- 0.0801 STD) through the use of 60.2% by weight calcium copper titanate and ReoFlex-60 silicone. The experiments suggest certain sensors developed in this work feasibly measure and distinguish between shear and compressional forces. Applications for such technology focus on improving quality of life in areas such as managing diabetic ulcer formation, preventing injuries, optimizing performance for athletes and military personnel, and augmenting the scope of motion capture in biomechanical studies.</p>