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The Scientific World Journal
Volume 2014, Article ID 459636, 6 pages
http://dx.doi.org/10.1155/2014/459636
Research Article

Classifying Normal and Abnormal Status Based on Video Recordings of Epileptic Patients

1Department of Electrical and Automatic Engineering, School of Information Engineering, Nanchang University, Nanchang 330031, China
2Department of Medical Biophysics, University of Western Ontario, Room E5-137, SJHC, 268 Grosvenor Street, London, ON, Canada N6A 4V2
3The Comprehensive Epilepsy Center, Departments of Neurology and Neurosurgery, Peking University People’s Hospital, Beijing 100044, China
4State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
5Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China

Received 27 February 2014; Accepted 27 March 2014; Published 8 April 2014

Academic Editors: Y.-B. Yuan and S. Zhao

Copyright © 2014 Jing Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Based on video recordings of the movement of the patients with epilepsy, this paper proposed a human action recognition scheme to detect distinct motion patterns and to distinguish the normal status from the abnormal status of epileptic patients. The scheme first extracts local features and holistic features, which are complementary to each other. Afterwards, a support vector machine is applied to classification. Based on the experimental results, this scheme obtains a satisfactory classification result and provides a fundamental analysis towards the human-robot interaction with socially assistive robots in caring the patients with epilepsy (or other patients with brain disorders) in order to protect them from injury.