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)
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.
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