Improving sEMG-based motion intention recognition for upper-limb amputees using transfer learning

Hand gesture recognition from multi-channel surface electromyography (sEMG) have been widely studied in the past decade. By analyzing muscle activities measured from forearm muscles, multiple hand gestures can be recognized. This technology can benefit upper-limb amputees in motion intention recognition, especially for those with trans-radial amputation, in terms of prosthesis control, rehabilitation and further human–computer interaction. However, due to the scarcity of signals collected from amputees, many related studies used signals from intact subjects as a proxy and result in overoptimistic classification performance. Comparing to sEMG signals from intact subjects, signals from upper-limb amputees suffer from signal quality deterioration which relates to the level of amputation and maybe other amputation information. Therefore, this study aims at improving the motion intention recognition performance in trans-radial amputated subjects. To tackle the challenges of data scarcity and signal quality deterioration, we propose a CNN-based transfer learning solution leveraging the knowledge learned from sEMG signals of intact subjects. The proposed method was developed from and tested with NinaPro database where 20 intact subjects and 11 amputees. We obtained 67.5% accuracy in the mDWT feature after transfer. And the results improved by 9.4% after transfer compared to no transfer in the RMS feature. In the end of the study, we further discussed the correlation between classification accuracy and amputation information including the percentage of remaining forearm and the number of years since amputation.