Low-cost multisensor integrated system for online walking gait detection

Yan, L ORCID: https://orcid.org/0000-0002-9986-2182, Wei, G ORCID: https://orcid.org/0000-0003-2613-902X, Hu, Z, Xiu, H, Wei, Y and Ren, L ORCID: https://orcid.org/0000-0003-3222-2102 2021, 'Low-cost multisensor integrated system for online walking gait detection' , Journal of Sensors, 2021 , p. 6378514.

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Abstract

A three-dimensional motion capture system is a useful tool for analysing gait patterns during walking or exercising, and it is frequently applied in biomechanical studies. However, most of them are expensive. This study designs a low-cost gait detection system with high accuracy and reliability that is an alternative method/equipment in the gait detection field to the most widely used commercial system, the virtual user concept (Vicon) system. The proposed system integrates mass-produced low-cost sensors/chips in a compact size to collect kinematic data. Furthermore, an x86 mini personal computer (PC) running at 100 Hz classifies motion data in real-time. To guarantee gait detection accuracy, the embedded gait detection algorithm adopts a multilayer perceptron (MLP) model and a rule-based calibration filter to classify kinematic data into five distinct gait events: heel-strike, foot-flat, heel-off, toe-off, and initial-swing. To evaluate performance, volunteers are requested to walk on the treadmill at a regular walking speed of 4.2 km/h while kinematic data are recorded by a low-cost system and a Vicon system simultaneously. The gait detection accuracy and relative time error are estimated by comparing the classified gait events in the study with the Vicon system as a reference. The results show that the proposed system obtains a high accuracy of 99.66% with a smaller time error (32 ms), demonstrating that it performs similarly to the Vicon system in the gait detection field.

Item Type: Article
Contributors: Ruiz, C (Editor)
Additional Information: ** From Hindawi via Jisc Publications Router ** Licence for this article: https://creativecommons.org/licenses/by/4.0/ **Journal IDs: eissn 1687-7268; pissn 1687-725X **Article IDs: publisher-id: 6378514 **History: archival-date 14-08-2021; published 14-08-2021; accepted 25-07-2021; rev-recd 02-07-2021; submitted 21-04-2021; published 2021
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Journal of Sensors
Publisher: Hindawi
ISSN: 1687-725X
Related URLs:
SWORD Depositor: Publications Router
Depositing User: Publications Router
Date Deposited: 23 Aug 2021 08:04
Last Modified: 28 Aug 2021 10:27
URI: http://usir.salford.ac.uk/id/eprint/61664

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