Gesture Recognition Using a Feature Fusion and Reconstruction Method under External Interference

Gesture recognition is crucial in fields such as exoskeleton and prosthetic control. A key factor affecting its practical effectiveness is how it can sustain high-quality gesture recognition performance under everyday external interferences. To address this challenge, this paper introduces a Force Myography (FMG) armband with adjustable sensor positioning, employing a cross-stretch design to ensure uniform force distribution across the armband. A human-machine coupling model was developed to analyze the impact of sensor position variations on data quality. Subsequently, an adjacent feature fusion and reconstruction method is proposed to map interfered signals to normal signals, thereby enhancing gesture recognition performance under interference. Five subjects participated in the experimental evaluation, undergoing tests both under interference and non-interference conditions. The results indicate that the uniform distribution of sensors along the arm improves data quality, achieving an accuracy exceeding 96.6%. The adjacent feature fusion and reconstruction method significantly enhances gesture recognition accuracy under interference, reaching 92.1%, and maintains a 96.2% effectiveness under non-interference. These results demonstrate the effectiveness of the proposed method, advancing the practical application of gesture recognition.