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MFC Histogram and Poincare Plot Images for Automated Gait Detection

Begg, Rezaul and Palaniswami, Marimuthu and Owen, Brendan and Taylor, Simon B and Dell'Oro, Lisa Ann (2004) MFC Histogram and Poincare Plot Images for Automated Gait Detection. In: Proceedings of the International Conference on Intelligent Sensing and Information Processing. IEEE, United States, pp. 368-372.

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Abstract

In this paper, we apply support vector machines (SVMs) for the automatic recognition of young-old gait from their respective gait-patterns. Minimum foot clearance (MFC) data of 30 young and 28 elderly participants were analysed using a PEAK-2D motion analysis system. Gait features extracted from individual MFC histogram-plot and Poincare-plots were used to develop gait classification models using SVMs. Test results indicate that the generalization performance of the SVMs, was on average 83.3% (±2.9) to differentiate young and elderly gait patterns. Forward feature selection algorithm demonstrated that only 3-5 gait features could differentiate the gait patterns with 90% accuracy. Performance of the gait classifier was evaluated using areas under the receiver operating characteristic plots. Improved performance of the classifier was evident when trained with reduced number of selected good features. These results suggest that SVMs are an efficient gait classifier for recognition of movement pattern changes due to ageing, and has potential for wider applications in gait diagnostics.

Item Type: Book Section
ISBN: 0780382439
Uncontrolled Keywords: data mining, feature extraction, foot, histograms, image motion analysis, motion analysis, senior citizens, support vector machine classification, support vector machines, testing, Poincare mapping
Subjects: FOR Classification > 0104 Statistics
FOR Classification > 1106 Human Movement and Sports Science
Faculty/School/Research Centre/Department > School of Sport and Exercise Science
Depositing User: Yimin Zeng
Date Deposited: 09 Apr 2013 03:45
Last Modified: 16 Mar 2015 05:05
URI: http://vuir.vu.edu.au/id/eprint/21415
DOI: 10.1109/ICISIP.2004.1287685
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Citations in Scopus: 4 - View on Scopus

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