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Detection of tripping gait patterns in the elderly using autoregressive features and support rector machines

Lai, Daniel and Begg, Rezaul and Taylor, Simon B and Palaniswami, Marimuthu (2008) Detection of tripping gait patterns in the elderly using autoregressive features and support rector machines. Journal of Biomechanics, 41 (8). pp. 1762-1772. ISSN 0021-9290

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Abstract

Elderly tripping falls cost billions annually in medical funds and result in high mortality rates often perpetrated by pulmonary embolism (internal bleeding) and infected fractures that do not heal well. In this paper, we propose an intelligent gait detection system (AR–SVM) for screening elderly individuals at risk of suffering tripping falls. The motivation of this system is to provide early detection of elderly gait reminiscent of tripping characteristics so that preventive measures could be administered. Our system is composed of two stages, a predictor model estimated by an autoregressive (AR) process and a support vector machine (SVM) classifier. The system input is a digital signal constructed from consecutive measurements of minimum toe clearance (MTC) representative of steady-state walking. The AR–SVM system was tested on 23 individuals (13 healthy and 10 having suffered at least one tripping fall in the past year) who each completed a minimum of 10min of walking on a treadmill at a self-selected pace. In the first stage, a fourth order AR model required at least 64 MTC values to correctly detect all fallers and non-fallers. Detection was further improved to less than 1min of walking when the model coefficients were used as input features to the SVM classifier. The system achieved a detection accuracy of 95.65% with the leave one out method using only 16 MTC samples, but was reduced to 69.57% when eight MTC samples were used. These results demonstrate a fast and efficient system requiring a small number of strides and only MTC measurements for accurate detection of tripping gait characteristics.

Item Type: Article
Uncontrolled Keywords: ResPubID16091, minimum toe clearance, support vector machines, autoregressive models, gait analysis, tripping falls
Subjects: Faculty/School/Research Centre/Department > Centre for Ageing, Rehabilitation, Exercise & Sport Science (CARES)
FOR Classification > 0903 Biomedical Engineering
SEO Classification > 9204 Public Health (excl. Specific Population Health)
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Depositing User: VUIR
Date Deposited: 02 Sep 2011 03:47
Last Modified: 16 Mar 2015 05:18
URI: http://vuir.vu.edu.au/id/eprint/3777
DOI: 10.1016/j.jbiomech.2008.02.037
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Citations in Scopus: 17 - View on Scopus

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