A Comprehensive Survey on Wearable Computing for Mental and Physical Health Monitoring

Wearable computing is evolving from a passive data collection paradigm into an active, precision-guided health orchestration system. This survey synthesizes developments across sensing modalities, wireless protocols, computational frameworks, and AI-driven analytics that collectively define the state of the art in mental and physical health monitoring. A narrative review methodology is used to map the landscape of hardware innovations—including microfluidic sweat sensing, smart textiles, and textile-embedded biosensing ecosystems—alongside advances in on-device AI acceleration, context-aware multimodal fusion, and privacy-preserving learning frameworks. The analysis highlights a shift toward multiplexed biochemical sensing for real-time metabolic profiling, neuromorphic and analog AI processors for ultra–low-power analytics, and closed-loop therapeutic systems capable of adapting interventions dynamically to both physiological and psychological states. These trends are examined in the context of emerging clinical and consumer use cases, with a focus on scalability, personalization, and data security. By grounding these insights in current research trajectories, this work positions wearable computing as a cornerstone of preventive, personalized, and participatory healthcare. Addressing identified technical and ethical challenges will be essential for the next generation of systems to become trusted, equitable, and clinically indispensable tools.