Identifying Abnormal Gait in Older People during Multiple-Tasks Assessment with Audio-Visual Cues

Rueangsirarak, Worasak, Uttama, Surapong, Kaewkaen, Kitchana and Shum, Hubert (2019) Identifying Abnormal Gait in Older People during Multiple-Tasks Assessment with Audio-Visual Cues. In: 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON): Chiang Rai, Thailand 18-21 July 2018. IEEE, Piscataway, NJ, pp. 780-783. ISBN 9781538635568, 9781538635551

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Rueangsirarak et al - Identifying Abnormal Gait in Older People during Multiple-Tasks Assessment with Audio-Visual Cues AAM.pdf - Accepted Version

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Official URL: https://doi.org/10.1109/ecticon.2018.8619931

Abstract

This research presents a feasibility to adopt a decision support system framework as a rehabilitation and assessment tool for supporting the physiotherapist in identifying the abnormal gaits of older people. The walking movement was captured by the Microsoft Kinect cameras in order to collect the human motion during 4-meters clinical walk test. 28 older adults participated in this research and perform their gait in front of the affordable cameras. To distinguish an abnormal gait with balance impairment from those of healthy older adults, two machine learning algorithms; ANN and SVM, were selected to classify the data. Experimental results show that SVM achieves the best performance of classification with 82.14% of accuracy, in single-task and double-task conditions, when compared with the standard clinical results. However, SVM cannot achieve an acceptable performance when classifying triple-task condition, achieving only 71.42% of accuracy. As a comparison, ANN delivers only 75.00% of accuracy, which is inferior to SVM. This study show that SVM can be considered as a rehabilitation measuring tool for assisting the physiotherapist in assessing the gait of older people.

Item Type: Book Section
Uncontrolled Keywords: gait analysis, abnormal gait, Kinect, support vector machine, artificial neural network, balance impairment
Subjects: B900 Others in Subjects allied to Medicine
G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
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Depositing User: Paul Burns
Date Deposited: 29 Aug 2018 11:03
Last Modified: 23 Apr 2019 14:00
URI: http://nrl.northumbria.ac.uk/id/eprint/35505

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