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Lookup NU author(s): Dr Agamemnon Krasoulis,
Professor Kianoush Nazarpour
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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 preclinical evaluation are required to assess the translational potential of the method.
Author(s): Krasoulis A, Nazarpour K
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Online publication date: 08/03/2022
Acceptance date: 04/03/2022
Date deposited: 28/03/2022
ISSN (print): 1534-4320
ISSN (electronic): 1558-0210
PubMed id: 35259109
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