The design and engineering of a fall and near-fall detection electronic textile

Rahemtulla, Z ORCID: https://orcid.org/0000-0002-4695-821X, Turner, A, Oliveira, C ORCID: https://orcid.org/0000-0001-8143-3534, Kaner, J ORCID: https://orcid.org/0000-0002-7946-7433, Dias, T and Hughes-Riley, T ORCID: https://orcid.org/0000-0001-8020-430X, 2023. The design and engineering of a fall and near-fall detection electronic textile. Materials, 16 (5): 1920. ISSN 1996-1944

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Abstract

Falls can be detrimental to the quality of life of older people, and therefore the ability to detect falls is beneficial, especially if the person is living alone and has injured themselves. In addition, detecting near falls (when a person is imbalanced or stumbles) has the potential to prevent a fall from occurring. This work focused on the design and engineering of a wearable electronic textile device to monitor falls and near-falls and used a machine learning algorithm to assist in the interpretation of the data. A key driver behind the study was to create a comfortable device that people would be willing to wear. A pair of over-socks incorporating a single motion sensing electronic yarn each were designed. The over-socks were used in a trial involving 13 participants. The participants performed three types of activities of daily living (ADLs), three types of falls onto a crash mat, and one type of near-fall. The trail data was visually analyzed for patterns, and a machine learning algorithm was used to classify the data. The developed over-socks combined with the use of a bidirectional long short-term memory (Bi-LSTM) network have been shown to be able to differentiate between three different ADLs and three different falls with an accuracy of 85.7%, ADLs and falls with an accuracy of 99.4%, and ADLs, falls, and stumbles (near-falls) with an accuracy of 94.2%. In addition, results showed that the motion sensing E-yarn only needs to be present in one over-sock.

Item Type:	Journal article
Publication Title:	Materials
Creators:	Rahemtulla, Z., Turner, A., Oliveira, C., Kaner, J., Dias, T. and Hughes-Riley, T.
Publisher:	MDPI AG
Date:	25 March 2023
Volume:	16
Number:	5
ISSN:	1996-1944
Identifiers:	Number Type 10.3390/ma16051920 DOI 1738140 Other
Rights:	© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > Nottingham School of Art & Design
Record created by:	Laura Ward
Date Added:	06 Mar 2023 10:54
Last Modified:	16 Dec 2024 11:55
Related URLs:	Dataset used by this publication Dataset used by this publication
URI:	https://irep.ntu.ac.uk/id/eprint/48459

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