Virtual hand rehabilitation training system based on neural network-assisted surface electromyography feature
Rixi Huang1, Guangjie Yu1, Zhongxian Ma1
1College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
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.
