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Point-to-point repetitive control of functional electrical stimulation for drop-foot

Point-to-point repetitive control of functional electrical stimulation for drop-foot
Point-to-point repetitive control of functional electrical stimulation for drop-foot
Drop-Foot is a common problem resulting from a range of neurological conditions, and prevents normal leg swing during gait, leading to abnormal, inefficient motion with an increased risk of falling. It damages the quality of life of over 122,000 people in the US and 11,400 people in the UK every year.
Functional electrical stimulation (FES) addresses drop-foot by artificially contracting the tibialis anterior, and has had considerable success both clinically and commercially. However current commercial controllers are open loop and have long set-up times. The few controllers in the research domain are predominantly open-loop, lack accuracy, and struggle with muscle delays, non-linearities and the onset of fatigue. More advanced controllers require extensive sensor data and/or are highly dependent on an identified model. Recent developments have shown model based controllers combined with learning can facilitate higher accuracy, however previous attempts employed batch-wise learning, and led to disjointed control signals. This paper applies repetitive control (RC) to drop-foot for the first time, facilitating a continuous, smooth process of learning with no resetting. To maximise performance, a comprehensive extension to the standard RC framework is undertaken to enable only isolated time points to be tracked, improving robustness and reducing memory and communication requirements. Experimental data confirms that RC can achieve normal gait when applied to FES-assisted gait with no voluntary effort. The new 'point-to-point' RC framework outperformed standard RC, while using only 5 data points per gait cycle and minimal control effort.
0967-0661
Page, A.P.
0c80d0ed-7cce-4346-8e85-8d63377ee58b
Freeman, C.T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Page, A.P.
0c80d0ed-7cce-4346-8e85-8d63377ee58b
Freeman, C.T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815

Page, A.P. and Freeman, C.T. (2020) Point-to-point repetitive control of functional electrical stimulation for drop-foot. Control Engineering Practice, 96, [104280]. (doi:10.1016/j.conengprac.2019.104280).

Record type: Article

Abstract

Drop-Foot is a common problem resulting from a range of neurological conditions, and prevents normal leg swing during gait, leading to abnormal, inefficient motion with an increased risk of falling. It damages the quality of life of over 122,000 people in the US and 11,400 people in the UK every year.
Functional electrical stimulation (FES) addresses drop-foot by artificially contracting the tibialis anterior, and has had considerable success both clinically and commercially. However current commercial controllers are open loop and have long set-up times. The few controllers in the research domain are predominantly open-loop, lack accuracy, and struggle with muscle delays, non-linearities and the onset of fatigue. More advanced controllers require extensive sensor data and/or are highly dependent on an identified model. Recent developments have shown model based controllers combined with learning can facilitate higher accuracy, however previous attempts employed batch-wise learning, and led to disjointed control signals. This paper applies repetitive control (RC) to drop-foot for the first time, facilitating a continuous, smooth process of learning with no resetting. To maximise performance, a comprehensive extension to the standard RC framework is undertaken to enable only isolated time points to be tracked, improving robustness and reducing memory and communication requirements. Experimental data confirms that RC can achieve normal gait when applied to FES-assisted gait with no voluntary effort. The new 'point-to-point' RC framework outperformed standard RC, while using only 5 data points per gait cycle and minimal control effort.

Text
Journal Paper One - RC ControllerV3_1_CF - Accepted Manuscript
Restricted to Repository staff only until 3 January 2021.
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More information

Accepted/In Press date: 13 December 2019
e-pub ahead of print date: 3 January 2020
Published date: March 2020

Identifiers

Local EPrints ID: 436879
URI: http://eprints.soton.ac.uk/id/eprint/436879
ISSN: 0967-0661
PURE UUID: 8a78422e-32c1-49e3-991c-6ca737aef935

Catalogue record

Date deposited: 13 Jan 2020 17:31
Last modified: 06 Oct 2020 17:02

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