An adaptive hand exoskeleton rehabilitation training system integrating virtual reality and an AI-based assessment engine.

Introduction: Post-stroke hand motor impairment is a major cause of long-term functional disability and reduced quality of life, with approximately 70% of stroke survivors experiencing persistent limitations in fine motor control. Conventional rehabilitation is constrained by low adherence, subjective assessment, and insufficient individualization, which limits exploitation of the neuroplasticity window for motor relearning. To address these challenges, we propose a bio-AI-VR integrated hand rehabilitation system that fuses biosignal sensing (bio), AI-based analysis, and virtual reality (VR) interaction to realize an efficient, adaptive, and quantifiable closed-loop training process. The integration rationale is grounded in three theoretical pillars: (i) multimodal data fusion theory-combining heterogeneous biosignal and behavioral data through AI to overcome single-modality limitations; (ii) closed-loop adaptive control theory-dynamically balancing challenge and capability via real-time feedback; (iii) neuroplasticity multisensory enhancement theory-coordinating visual, proprioceptive, and motor pathways to strengthen cortical reorganization. This work addresses three testable hypotheses: (RQ1) Can multimodal biosignal fusion achieve real-time assessment with Methods: A lightweight hand exoskeleton ( Results: All 24 participants completed the study with one missed session (479 of 480 scheduled sessions, 8,946 annotated segments); end-to-end latency median 38 ms (IQR 33-42 ms), decomposed as: sampling Discussion: Results validate all three hypotheses: (i) multimodal fusion exceeded technical targets (