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Myoelectric Control Performance of Two Degree of Freedom Hand-Wrist Prosthesis by Able-Bodied and Limb-Absent Subjects.

Authors :
Zhu, Ziling
Li, Jianan
Boyd, William J.
Martinez-Luna, Carlos
Dai, Chenyun
Wang, Haopeng
Wang, He
Huang, Xinming
Farrell, Todd R.
Clancy, Edward A.
Source :
IEEE Transactions on Neural Systems & Rehabilitation Engineering; 2022, Vol. 30, p893-904, 12p
Publication Year :
2022

Abstract

Recent research has advanced two degree-of-freedom (DoF), simultaneous, independent and proportional control of hand-wrist prostheses using surface electromyogram signals from remnant muscles as the control input. We evaluated two such regression-based controllers, along with conventional, sequential two-site control with co-contraction mode switching (SeqCon), in box-block, refined-clothespin and door-knob tasks, on 10 able-bodied and 4 limb-absent subjects. Subjects operated a commercial hand and wrist using a socket bypass harness. One 2-DoF controller (DirCon) related the intuitive hand actions of open-close and pronation-supination to the associated prosthesis hand-wrist actions, respectively. The other (MapCon) mapped myoelectrically more distinct, but less intuitive, actions of wrist flexion-extension and ulnar-radial deviation. Each 2-DoF controller was calibrated from separate 90 s calibration contractions. SeqCon performed better statistically than MapCon in the predominantly 1-DoF box-block task (>20 blocks/minute vs. 8–18 blocks/minute, on average). In this task, SeqCon likely benefited from an ability to easily focus on 1-DoF and not inadvertently trigger co-contraction for mode switching. The remaining two tasks require 2-DoFs, and both 2-DoF controllers each performed better (factor of 2–4) than SeqCon. We also compared the use of 12 vs. 6 optimally-selected EMG electrodes as inputs, finding no statistical difference. Overall, we provide further evidence of the benefits of regression-based EMG prosthesis control of 2-DoFs in the hand-wrist. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15344320
Volume :
30
Database :
Complementary Index
Journal :
IEEE Transactions on Neural Systems & Rehabilitation Engineering
Publication Type :
Academic Journal
Accession number :
170416061
Full Text :
https://doi.org/10.1109/TNSRE.2022.3163149