In Brief:
Eight healthy subjects wearing a prosthetic socket emulator show that (1) variations in the weight of the prosthesis, and (2) upper-arm movements significantly influence the robustness of a traditional classifier based on k-nn algorithm, causing a significant drop in performance. It is demonstrated in simulated conditions that traditional pattern recognition does not allow the separation of the effects of the weight of the prosthesis because a surface recorded EMG pattern due only to the 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.
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