Decision-centered wearable biosensors for personalized rehabilitation: integrating multimodal monitoring, artificial intelligence, and closed-loop intervention
Abstract
Personalized rehabilitation requires repeated assessment of movement, physiological tolerance, fatigue, and adherence, yet conventional clinic-based measurements provide only intermittent snapshots. The convergence of wearable biosensors with artificial intelligence, edge-cloud computing, and connected data infrastructures creates an opportunity to convert continuous multimodal signals into timely rehabilitation decisions. This critical narrative review synthesizes physiological, biochemical, electrophysiological, biomechanical, and behavioral sensing within a monitoring-interpretation-feedback-adaptation-outcome framework. We first evaluate analytical validity, real-world robustness, biocompatibility, wearability, calibration drift, and power continuity. We then examine signal-quality control, multimodal fusion, digital biomarkers, federated learning, digital twins, and predictive models according to latency, uncertainty, generalizability, privacy, and decision utility. The review follows biosensor-derived information across a graded spectrum of action, from clinician-reviewed monitoring and real-time biofeedback to assist-as-needed adaptation, functional electrical stimulation, and state-responsive neuromodulation. Applications include motor and physiological assessment, fatigue-aware dose adjustment, musculoskeletal and neurological rehabilitation, posture correction, sensor-rich prosthetics, and Internet of Things-enabled home rehabilitation. Across these settings,…
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