Journal of hand therapy : official journal of the American Society of Hand Therapists · 2026
Virtual hand rehabilitation training system based on neural network-assisted surface electromyography feature extraction and analysis.
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Huang Rixi, Yu Guangjie, Ma Zhongxian, Zheng Ying, Zhang Yue, He Bingwei, Chen Xin-Yuan
Аннотация
BACKGROUND: Traditional hand rehabilitation relies heavily on professional therapists, which brings great challenges and limitations to patients with hand dysfunction, making convenient and flexible home rehabilitation difficult to achieve.
PURPOSE: This study aims to develop an innovative virtual hand rehabilitation system to improve patients' finger motor coordination and provide an effective, convenient home rehabilitation solution.
STUDY DESIGN: This study adopted a technical development and preliminary verification design. A targeted virtual rehabilitation system was developed and preliminarily validated through patient participation trials.
METHODS: Based on the Unity platform and C# scripting, the system integrates VR technology, sEMG real-time signal monitoring and LSTM neural networks. Three virtual rehabilitation game scenarios were constructed to realize interactive training and accurate gesture recognition.
RESULTS: The system achieves over 90% accuracy in recognizing eight complex hand gestures. Ten patients with hand dysfunction participated in the test, and all feedback confirmed improved rehabilitation engagement, safety and convenience.
CONCLUSIONS: The proposed system has reliable recognition performance and good user experience. It offers a novel and feasible approach for home-based fine motor rehabilitation of hand dysfunction patients with broad application potential.
PURPOSE: This study aims to develop an innovative virtual hand rehabilitation system to improve patients' finger motor coordination and provide an effective, convenient home rehabilitation solution.
STUDY DESIGN: This study adopted a technical development and preliminary verification design. A targeted virtual rehabilitation system was developed and preliminarily validated through patient participation trials.
METHODS: Based on the Unity platform and C# scripting, the system integrates VR technology, sEMG real-time signal monitoring and LSTM neural networks. Three virtual rehabilitation game scenarios were constructed to realize interactive training and accurate gesture recognition.
RESULTS: The system achieves over 90% accuracy in recognizing eight complex hand gestures. Ten patients with hand dysfunction participated in the test, and all feedback confirmed improved rehabilitation engagement, safety and convenience.
CONCLUSIONS: The proposed system has reliable recognition performance and good user experience. It offers a novel and feasible approach for home-based fine motor rehabilitation of hand dysfunction patients with broad application potential.