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AI-Driven Wearable Sensing Systems for Human Well-being https://doi.org/10.17863/CAM.125296 Tang, Chenyu 0000000263685639 Occhipinti, Luigi Wearable sensing technologies are evolving from simple activity trackers into intelligent, multimodal systems that can continuously assess and enhance human well-being. Traditional devices remain largely passive, limited by heterogeneous data, energy constraints, and opaque AI models. This thesis, titled AI-Driven Wearable Sensing Systems for Human Well-being, advances a new generation of wearable platforms that transform raw physiological and behavioral signals into real-time, adaptive, and interpretable decisions. Three interconnected domains are addressed. For mobility, multimodal systems combining textile EMG, IMU, and strain sensors enable robust gesture recognition, human activity monitoring, and physiologically informed exoskeleton control. For communication, wearable silent speech interfaces integrate throat strain and multi-channel EMG sensors with efficient neural decoders and language-model–assisted expansion, enabling natural, low-latency expression for both healthy users and individuals with dysarthria. For health monitoring, smart garments and multimodal home platforms provide continuous tracking of sleep, rehabilitation progress, and daily health states, moving beyond passive recording toward proactive, personalized guidance. These contributions converge in a future roadmap toward interpretable human body digital twins, where cross-scale sensing and adaptive AI together support trustworthy, personalized, and continuous health management. By uniting sensor innovation, embedded intelligence, and human-centered design, this thesis establishes a framework for AI-driven wearable systems that actively promote human well-being in both clinical and everyday contexts. University of Cambridge 2025-10-17 Thesis Doctoral Doctor of Philosophy (PhD) eng https://www.repository.cam.ac.uk/handle/1810/395965 https://www.repository.cam.ac.uk/bitstreams/1e7305f5-e8e6-47da-b4f7-8dd2e8292d53/download e1375c13d24e28a57a91bcbc62e2bf1d https://www.repository.cam.ac.uk/bitstreams/71adbf77-d8dc-4b9e-bbea-5bf57262fd8a/download 87eda9de84448d1f82354d60eee3eb5f http://purl.org/NET/rdflicense/allrightsreserved Wearable Sensor Machine Learning Healthcare Human-Machine-Interaction Robotics