The effect of calibration parameters on the control of a myoelectric hand prosthesis using EMG feedback

Objective: The implementation of somatosensory feedback in upper limb myoelectric prostheses is an important step towards the restoration of lost sensory-motor functions. EMG feedback is a recently proposed method for closing the control loop wherein the myoelectric signal that drives the prosthesis is also used to generate the feedback provided to the user. Therefore, the characteristics of the myoelectric signal (variability and sensitivity) are likely to significantly affect the subject's ability to utilize feedback for online control. In the present study, we investigated how the cutoff frequency of the low-pass filter (0.5, 1 and 1.5 Hz) and normalization value (20, 40 and 60% of maximum voluntary contraction) affect the quality of closed-loop control with EMG feedback. Lower cutoff and normalization decrease the variability of EMG but also increase the time lag between the contraction and the feedback (cutoff) as well as the sensitivity of myoelectric signal (normalization). Approach: Ten participants were asked to generate three grasp force levels with a myoelectric prosthetic hand, while receiving 5-level vibrotactile EMG feedback, over nine experimental runs (all parameter combinations). The outcome measure was the success rate in achieving the appropriate level of myoelectric signal (primary outcome) and grasping force (secondary outcome). Main Results: Overall, the experiments demonstrated that EMG feedback provided robust control across conditions. Nevertheless, the performance was significantly better for the lowest cutoff (0.5 Hz) and higher normalization (40 and 60%). The highest success rate for the EMG was 72% in the condition (40% MVC, 0.5 Hz), which was 24% higher than that in the condition (20% MVC, 1.5 Hz) with the lowest performance. The success rate for the force followed a similar trend. Significance: This is the first study that systematically explored the parameter space for calibration of EMG feedback, which is a critical step for the future clinical application.