Quantifying Pattern Recognition-Based Myoelectric Control of Multifunctional Transradial Prostheses

We evaluated real-time myoelectric pattern recognition control of a virtual arm by transradial amputees. Five unilateral patients performed 10 wrist and hand movements using their amputated and intact arms. In order to demonstrate the value of information from intrinsic hand muscles, this data was included in EMG recordings from the intact arm. With both arms, motions were selected in approximately 0.2 s on average, and completed in less than 1.25 s. Approximately 99% of wrist movements were completed using either arm; however, the completion rate of hand movements was significantly lower for the amputated arm (53.9% +/- 14.2%) than for the intact arm (69.4% +/- 13.1%). For the amputated arm, average classification accuracy for only 6 movements-including a single hand grasp-was 93.1% +/- 4.1%, compared to 84.4% +/- 7.2% for all 10 movements. Use of 6 optimally-placed electrodes only reduced this accuracy to 91.5% +/- 4.9%. These results suggest that muscles in the residual forearm produce sufficient myoelectric information for real-time wrist control, but not for performing multiple hand grasps. The outcomes of this study could aid the development of a practical multifunctional myoelectric prosthesis for transradial amputees, and suggest that increased EMG information-such as made available through targeted muscle reinnervation-could improve control of these prostheses.