Intuitive neuromyoelectric control of a dexterous bionic arm using a modified Kalman filter.

Autor: George JA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, 84112, United States. Electronic address: Jacob.George@utah.edu., Davis TS; Department of Neurosurgery, University of Utah, Salt Lake City, UT, 84112, United States. Electronic address: Tyler.Davis@hsc.utah.edu., Brinton MR; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, 84112, United States. Electronic address: Mark.Brinton@utah.edu., Clark GA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, 84112, United States. Electronic address: Greg.Clark@utah.edu.
Jazyk: angličtina
Zdroj: Journal of neuroscience methods [J Neurosci Methods] 2020 Jan 15; Vol. 330, pp. 108462. Date of Electronic Publication: 2019 Nov 08.
DOI: 10.1016/j.jneumeth.2019.108462
Abstrakt: Background: Multi-articulate prostheses are capable of performing dexterous hand movements. However, clinically available control strategies fail to provide users with intuitive, independent and proportional control over multiple degrees of freedom (DOFs) in real-time.
New Method: We detail the use of a modified Kalman filter (MKF) to provide intuitive, independent and proportional control over six-DOF prostheses such as the DEKA "LUKE" arm. Input features include neural firing rates recorded from Utah Slanted Electrode Arrays and mean absolute value of intramuscular electromyographic (EMG) recordings. Ad-hoc modifications include thresholds and non-unity gains on the output of a Kalman filter.
Results: We demonstrate that both neural and EMG data can be combined effectively. We also highlight that modifications can be optimized to significantly improve performance relative to an unmodified Kalman filter. Thresholds significantly reduced unintended movement and promoted more independent control of the different DOFs. Gains were significantly greater than one and served to ease movement initiation. Optimal modifications can be determined quickly offline and translate to functional improvements online. Using a portable take-home system, participants performed various activities of daily living.
Comparison With Existing Methods: In contrast to pattern recognition, the MKF allows users to continuously modulate their force output, which is critical for fine dexterity. The MKF is also computationally efficient and can be trained in less than five minutes.
Conclusions: The MKF can be used to explore the functional and psychological benefits associated with long-term, at-home control of dexterous prosthetic hands.
(Copyright © 2019 The Author(s). Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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