Abstract
We aim to develop a paradigm for simultaneous and independent control of multiple degrees of freedom (DOFs) for upper-limb prostheses. To that end, we introduce action control, a novel method to operate prosthetic digits with surface electromyography (EMG) based on multi-output, multi-class classification. At each time step, the decoder classifies movement intent for each controllable DOF into one of three categories: open, close, or stall (i.e., no movement). We implemented a real-time myoelectric control system using this method and evaluated it by running experiments with one unilateral and two bilateral amputees. Participants controlled a six-DOF bar interface on a computer display, with each DOF corresponding to a motor function available in multi-articulated prostheses. We show that action control can significantly and systematically outperform the state-of-the-art method of position control via multi-output regression in both task- and non-task-related measures. Using the action control paradigm, improvements in median task performance over regression-based control ranged from 20.14% to 62.32% for individual participants. Analysis of a post-experimental survey revealed that all participants rated action higher than position control in a series of qualitative questions and expressed an overall preference for the former. Action control has the potential to improve the dexterity of upper-limb prostheses. In comparison with regression-based systems, it only requires discrete instead of real-valued ground truth labels, typically collected with motion tracking systems. This feature makes the system both practical in a clinical setting and also suitable for bilateral amputation. This work is the first demonstration of myoelectric digit control in bilateral upper-limb amputees. Further investigation and pre-clinical evaluation are required to assess the translational potential of the method.
Highlights
M odern hand prostheses offer the potential of partially restoring the functionality of a missing upper-limb
For participant P2, who took part in two experimental sessions, one with each side, there were no significant differences in performance when using the right and left sides
We have evaluated action control, a novel paradigm for EMG-based prosthesis digit control based on multi-output classification
Summary
M odern hand prostheses offer the potential of partially restoring the functionality of a missing upper-limb. They are typically controlled by muscular activity signals recorded on the skin surface using electromyography (EMG) signals. Holy grail of upper-limb prosthetics research is the simultaneous and independent control of multiple degrees of freedom (DOFs), including wrist and digit artificial joints [1]. This currently seems as the only way to approximate the remarkable dexterity of the human hand, which is still considered as the nature’s most versatile end-effector [2]
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