A new strategy based on feature filtering technique for improving the real-time control performance of myoelectric prostheses

Abstract Surface electromyogram pattern recognition (EMG-PR) has been considered as a promising approach for predicting amputees’ motion intentions to control myoelectric prostheses. However, EMG recordings are mostly contaminated by various interferences, which decay the motion prediction of EMG-PR methods, thus affecting the control performance of the prosthesis. One common way to solve this issue is to improve the quality of EMG signals via filtering methods. Unfortunately, complete attenuation of effects caused by contaminated EMG recordings remains a major challenge till date. In this study, besides the EMG signal filtering, an additional signal conditioning strategy, designated as feature filtering, was proposed and applied to the features that were extracted from EMG signals to improve the overall performance of EMG-PR. The proposed strategy’s performance was investigated in both offline analysis and real-time prosthesis control test for able bodied subjects and amputees. The experimental results showed that, by using the proposed feature filtering strategy, the offline motion classification accuracy was increased by 6.4% and 7.4% for able bodied subjects and amputees, respectively, and the real-time motion completion rate was increased by 12.5% and 14.6%. Furthermore, the performance of feature filtering strategy was compared with that of a commonly used post-processing strategy known as majority vote. It was observed that the proposed strategy achieved significantly better performance in both offline motion classification and real-time prosthetic control. Findings of this study suggest that the proposed strategy could potentially enhance the robustness and reliability of EMG-PR method in the control of prostheses and other human–machine interaction systems.