Virtual hand rehabilitation training system based on neural network-assisted surface electromyography feature extraction and analysis.
Abstract
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.