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A machine learning approach for automated recognition of movement patterns using basis, kinetic and kinematic gait data

Begg, Rezaul and Kamruzzaman, Joarder (2005) A machine learning approach for automated recognition of movement patterns using basis, kinetic and kinematic gait data. Journal of Biomechanics, 38 (3). pp. 401-408. ISSN 0021-9290

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This paper investigated application of a machine learning approach (Support vector machine, SVM) for the automatic recognition of gait changes due to ageing using three types of gait measures: basic temporal/spatial, kinetic and kinematic. The gaits of 12 young and 12 elderly participants were recorded and analysed using a synchronized PEAK motion analysis system and a force platform during normal walking. Altogether, 24 gait features describing the three types of gait characteristics were extracted for developing gait recognition models and later testing of generalization performance. Test results indicated an overall accuracy of 91.7% by the SVM in its capacity to distinguish the two gait patterns. The classification ability of the SVM was found to be unaffected across six kernel functions (linear, polynomial, radial basis, exponential radial basis, multi-layer perceptron and spline). Gait recognition rate improved when features were selected from different gait data type. A feature selection algorithm demonstrated that as little as three gait features, one selected from each data type, could effectively distinguish the age groups with 100% accuracy. These results demonstrate considerable potential in applying SVMs in gait classification for many applications.

Item Type: Article
Uncontrolled Keywords: gait, support vector machine, gait classification, elderly
Subjects: Historical > RFCD Classification > 290000 Engineering and Technology
Historical > RFCD Classification > 320000 Medical and Health Sciences
Historical > Faculty/School/Research Centre/Department > Centre for Ageing, Rehabilitation, Exercise & Sport Science (CARES)
Depositing User: Ms Phung T Tran
Date Deposited: 03 Mar 2009 16:59
Last Modified: 06 Oct 2020 00:37
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Citations in Scopus: 196 - View on Scopus

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