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Adaptive Auto-Regressive Proportional Myoelectric Control

Published on Jan 23, 2019in IEEE Transactions on Neural Systems and Rehabilitation Engineering3.478
· DOI :10.1109/TNSRE.2019.2894464
Carles Igual1
Estimated H-index: 1
(Polytechnic University of Valencia),
Carles Igual1
Estimated H-index: 1
(Polytechnic University of Valencia)
+ 1 AuthorsLucas C. Parra54
Estimated H-index: 54
(CCNY: City College of New York)
Abstract
In proportional myographic control, one can control either position or velocity of movement. Here, we propose to use adaptive auto-regressive filters, so as to gradually adjust between the two. We implemented this in an adaptive system with closed-loop feedback, where both the user and the machine simultaneously attempt to follow a cursor on a 2-D arena. We tested this on 15 able-bodied and three limb-deficient participants using an eight-channel myoelectric armband. The human–machine pairs learn to perform smoother cursor movements with a larger range of motion when using the auto-regressive filters, as compared with our previous effortswithmoving-average filters. Importantly, the human–machine system converges to an approximate velocity control strategy resulting in faster and more accuratemovements with lessmuscle effort. The method is not specific tomyoelectriccontroland could be used equally well for motion control using high-dimensional signals from reinnervatedmuscles or direct brain recordings.
  • References (32)
  • Citations (4)
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References32
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Myoelectric hand prostheses are usually controlled with two bipolar electrodes located on the flexor and extensor muscles of the residual limb. With clinically established techniques, only one function can be controlled at a time. This is cumbersome and limits the benefit of additional functions offered by modern prostheses. Extensive research has been conducted on more advanced control techniques, but the clinical impact has been limited, mainly due to the lack of reliability in real-world cond...
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Objective. To compensate for a limb lost in an amputation, myoelectric prostheses use surface electromyography (EMG) from the remaining muscles to control the prosthesis. Despite considerable progress, myoelectric controls remain markedly different from the way we normally control movements, and require intense user adaptation. To overcome this, our goal is to explore concurrent machine co-adaptation techniques that are developed in the field of brain-machine interface, and that are beginning to...
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Abstract A training strategy for simultaneous and proportional myoelectric control of multiple degrees of freedom (DOFs) is proposed. Ten subjects participated in this work in which wrist flexion–extension, abduction–adduction, and pronation–supination were investigated. Subjects were prompted to elicit contractions corresponding and proportional to the excursion of a moving cursor on a computer screen. Artificial neural networks (ANNs) were used to map the electromyogram (EMG) signals obtained ...
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