title: Classifying normal and abnormal status based on video recordings of epileptic patients.
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.
authors: Department of Electrical and Automatic Engineering, School of Information Engineering, Nanchang University, Nanchang 330031, China.
authors: Jing
authors: J
authors: Li
authors: Department of Medical Biophysics, University of Western Ontario, Room E5-137, SJHC, 268 Grosvenor Street, London, ON, Canada N6A 4V2.
authors: Xiantong
authors: X
authors: Zhen
authors: The Comprehensive Epilepsy Center, Departments of Neurology and Neurosurgery, Peking University People's Hospital, Beijing 100044, China.
authors: Xianzeng
authors: X
authors: Liu
authors: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China ; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China.
authors: Gaoxiang
authors: 0000-0002-6390-626X
authors: G
authors: Ouyang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004827:Epilepsy / Q000145:classification* / Q000175:diagnosis*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods*; D014741:Video Recording / Q000379:methods*