Influence of the weight actions of the hand prosthesis on the performance of pattern recognition based myoelectric control: Preliminary study

In transradial amputees, the muscles in the residual forearm naturally employed by the unimpaired for flexing/extending the hand fingers, are the most appropriate targets, for multi-fingered prostheses control. However, once the prosthetic socket is manufactured and fitted on the residual forearm, the recorded EMG might not be originated only by the intention of performing finger movements, but also by the muscular activity needed to sustain the prosthesis itself. In this work, we preliminary show on healthy subjects wearing a prosthetic socket emulator that (i) variations in the weight of the prosthesis, and (ii) upper arm movements significantly influence the robustness of a traditional classifier based on k-nn algorithm. We show in simulated conditions that traditional pattern recognition systems do not allow the separation of the effects of the weight of the prosthesis because a surface recorded EMG pattern caused by the simple lifting or moving of the prosthesis is misclassified into a hand control movement. This suggests that a robust classifier should add to myoelectric signals, inertial transducers like multi-axes position, acceleration sensors or sensors able to monitor the interaction forces between the socket and the end-effector.