The classification of multiple interacting gait abnormalities using insole sensors and machine learning

Turner, A, Scott, D and Hayes, S ORCID: https://orcid.org/0000-0003-0767-3657, 2022. The classification of multiple interacting gait abnormalities using insole sensors and machine learning. In: Ahamed, SI, Ardagna, CA, Bian, H, Bochicchio, M, Chang, CK, Chang, RN, Damiani, E, Liu, L, Pavel, M, Priami, C, Shahriar, H, Ward, R, Xhafa, F, Zhang, J and Zulkernine, F, eds., Proceedings of the 2022 IEEE International Conference On Digital Health (IEEE ICDH 2022). Hybrid Conference Barcelona, Spain 11-15 July 2022. Piscataway, NJ: Institute of Electrical and Electronics Engineers. ISBN 9781665481496

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Abstract

In this work we investigate the effectiveness of a wireless in-shoe pressure sensing system used in combination with a type of machine learning referred to as long term short term memory networks (LSTMs) to classify multiple interacting gait perturbations. Artificially induced gait perturbations consisted of restricted knee extension and altered under foot centre of pressure (COP). The primary aim was to assess the capacity to diagnose gait abnormalities without the need to attend a gait laboratory or visit a clinical healthcare professional, through the use of technology. Ultimately, such a system could be used to autonomously generate therapeutic guidance and provide healthcare professionals with accurate up to date information about a patients gait. The results show that LSTMs are capable of classifying complex interacting gait perturbations using in-shoe pressure data. When testing, 11 of 12 perturbation conditions were correctly classified overall and 58.8% of all data instances were correctly classified (8.3% is random classification). This work illustrates that an automated low cost, non-invasive gait diagnosis system with minimal sensors can be used to identify interacting gait abnormalities in individuals and has further potential to be used in a healthcare setting.

Item Type:	Chapter in book
Description:	Paper presented at the 2022 IEEE International Conference on Digital Health (IEEE ICDH 2022), Barcelona, Spain, 10-16 July 2022.
Creators:	Turner, A., Scott, D. and Hayes, S.
Publisher:	Institute of Electrical and Electronics Engineers
Place of Publication:	Piscataway, NJ
Date:	July 2022
ISBN:	9781665481496
Identifiers:	Number Type 10.1109/icdh55609.2022.00020 DOI 1593457 Other
Rights:	Copyright © 2022 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved. Copyright and Reprint Permissions: Abstracting is permitted with credit to the source. Libraries may photocopy beyond the limits of US copyright law, for private use of patrons, those articles in this volume that carry a code at the bottom of the first page, provided that the per-copy fee indicated in the code is paid through the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923.
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	05 Sep 2022 14:03
Last Modified:	05 Sep 2022 14:03
URI:	https://irep.ntu.ac.uk/id/eprint/46963

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