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Iterative learning control of FES with embedded simultaneous volitional EMG

Iterative learning control of FES with embedded simultaneous volitional EMG
Iterative learning control of FES with embedded simultaneous volitional EMG
Seventeen million people are left with limb impairment following stroke, and there is an urgent need for new effective rehabilitation technology. Functional electrical stimulation (FES) has been shown to facilitate motor re-learning by artificially activating muscles during practice of motor tasks, however clinical outcomes depend on how closely FES matches the intended motion. Electromyography (EMG) signals recorded from muscle can capture voluntary intention, and have successfully been employed in openloop FES controllers. Likewise, model-based controllers using force and/or position data have yielded accurate movement control in clinical trials. Iterative learning control (ILC) in particular has been successful in clinical tests, since the process of rehabilitation
is inherently iterative. This paper develops a new control strategy to combine both EMG and model-based control. It begins by developing a novel hybrid model of muscle dynamics incorporating both EMG and FES. ILC is then employed to enable precise control over both variables, thereby offering substantial improvements over existing control schemes in the domain of stroke rehabilitation. Experimental results confirm efficacy of the hybrid control scheme, as well as its suitability for clinical application.
IEEE
Sa-E, Sakariya
30bb2dfc-cc97-4c38-81f8-42273fd005e2
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Yang, Kai
f1c9b81d-e821-47eb-a69e-b3bc419de9c7
Sa-E, Sakariya
30bb2dfc-cc97-4c38-81f8-42273fd005e2
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Yang, Kai
f1c9b81d-e821-47eb-a69e-b3bc419de9c7

Sa-E, Sakariya, Freeman, Christopher and Yang, Kai (2019) Iterative learning control of FES with embedded simultaneous volitional EMG. In IEEE 58th Conference on Decision and Control. IEEE. 6 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

Seventeen million people are left with limb impairment following stroke, and there is an urgent need for new effective rehabilitation technology. Functional electrical stimulation (FES) has been shown to facilitate motor re-learning by artificially activating muscles during practice of motor tasks, however clinical outcomes depend on how closely FES matches the intended motion. Electromyography (EMG) signals recorded from muscle can capture voluntary intention, and have successfully been employed in openloop FES controllers. Likewise, model-based controllers using force and/or position data have yielded accurate movement control in clinical trials. Iterative learning control (ILC) in particular has been successful in clinical tests, since the process of rehabilitation
is inherently iterative. This paper develops a new control strategy to combine both EMG and model-based control. It begins by developing a novel hybrid model of muscle dynamics incorporating both EMG and FES. ILC is then employed to enable precise control over both variables, thereby offering substantial improvements over existing control schemes in the domain of stroke rehabilitation. Experimental results confirm efficacy of the hybrid control scheme, as well as its suitability for clinical application.

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More information

Published date: 1 December 2019
Venue - Dates: IEEE 58th Conference on Decision and Control, France, 2019-12-11 - 2019-12-13

Identifiers

Local EPrints ID: 436761
URI: http://eprints.soton.ac.uk/id/eprint/436761
PURE UUID: 99626f71-7dc0-4484-9ce3-c621e7b13026

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Date deposited: 03 Jan 2020 17:30
Last modified: 08 Jan 2020 17:32

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