A Novel Sliding Mode Differentiator-Based Feature for EMG-Based Hand Gesture Characterization

Motor intent (MI)-based muscle computer interfaces (MCIs) have been widely explored for prosthetic control as a means of restoring functionality to the lost limb in amputees. However, persistent issues remain, such as insufficient robustness of the current features, the presence of coherent noise, and the low spatial resolution of electromyography (EMG) sensors. Subsequently, these results in decreased movement characterization performance. Therefore, this study introduces a novel feature extraction technique that utilizes a Sliding Mode Differentiator (SMD) to extract unique patterns from EMG signals, followed by the deployment of symmetric positive definite matrices (SPD) to efficiently leverage the spatial-temporal properties of the EMG signal. The average classification results of 98.7±3.0% and 97.9±5.2% for 21 able-bodied subjects and 15 amputees respectively, suggests an improvement in accuracy for characterizing 13 hand gestures, thereby outperforming other state-of-the-art feature methods. Further, the channel optimization analysis shows that the number of channels can be reduced by 75% (from 24 channels to 6 channels) without compromising the performance of the proposed technique. This justifies the potential of the proposed technique in both high-density and sparse-density EMG electrode configurations. Additional analysis of the performance of the techniques in the presence of noise indicates that the proposed method can significantly outperform other methods. Therefore, the findings of this study have the potential to significantly improve the control performance of prostheses, rehabilitation assistive robots, and hand gesture-related games.