A DeepSeek Assistant Virtual Rehabilitation System for Upper-Limb Exercise

Stroke often leads to significant upper-limb motor impairments, necessitating long-term rehabilitation. However, traditional methods depend on therapist supervision or complex robotic systems, which limit their accessibility and scalability. To address these issues, this study proposes an AI-driven virtual rehabilitation system comprising real-time posture tracking, a virtual doctor with voice interaction, and a plug-in game to enhance engagement. Specifically, the proposed system architecture leverages a ZED 2i Stereo camera for real-time motion tracking and incorporates a DeepSeek assistant module to provide personalized feedback and adjust game difficulty levels. A proof-of-concept experiment involving a healthy participant was conducted to validate the system's architectural integrity, realtime latency, and interaction logic. Preliminary results confirm the system's technical feasibility and suggest it holds promise for future clinical use, pending further validation and optimisation.