Support vector machines for automated gait classification

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Begg, Rezaul, Palaniswami, M and Owen, B (2005) Support vector machines for automated gait classification. IEEE Transactions on Biomedical Engineering, 52 (5). pp. 828-838. ISSN 0018-9294

Abstract

Ageing influences gait patterns causing constant threats to control of locomotor balance. Automated recognition of gait changes has many advantages including, early identification of at-risk gait and monitoring the progress of treatment outcomes. In this paper, we apply an artificial intelligence technique [support vector machines (SVM)] for the automatic recognition of young-old gait types from their respective gait-patterns. Minimum foot clearance (MFC) data of 30 young and 28 elderly participants were analyzed using a PEAK-2D motion analysis system during a 20-min continuous walk on a treadmill at self-selected walking speed. Gait features extracted from individual MFC histogram-plot and Poincaré-plot images were used to train the SVM. Cross-validation test results indicate that the generalization performance of the SVM was on average 83.3% (′2.9) to recognize young and elderly gait patterns, compared to a neural network's accuracy of 75.0 ′ 5.0%. A "hill-climbing" feature selection algorithm demonstrated that a small subset (3-5) of gait features extracted from MFC plots 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 and with radial basis function kernel. These results suggest that SVMs can function as an efficient gait classifier for recognition of young and elderly gait patterns, and has the potential for wider applications in gait identification for falls-risk minimization in the elderly.

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Item type Article
URI https://vuir.vu.edu.au/id/eprint/1764
DOI https://doi.org/10.1109/TBME.2005.845241
Official URL http://dx.doi.org/10.1109/TBME.2005.845241
Subjects Historical > RFCD Classification > 290000 Engineering and Technology
Historical > RFCD Classification > 320000 Medical and Health Sciences
Historical > FOR Classification > 1004 Medical Biotechnology
Historical > FOR Classification > 1106 Human Movement and Sports Science
Historical > Faculty/School/Research Centre/Department > Centre for Ageing, Rehabilitation, Exercise & Sport Science (CARES)
Keywords feature selection, gait analysis, histogram, minimum foot clearance, poincare plot, support vector machines
Citations in Scopus 265 - View on Scopus
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