Classifying Normal and Abnormal Status Based on Video Recordings of Epileptic Patients
Joint Authors
Ouyang, Gaoxiang
Li, J.
Zhen, Xiantong
Liu, Xianzeng
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-08
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Li, J.& Zhen, Xiantong& Liu, Xianzeng& Ouyang, Gaoxiang. 2014. Classifying Normal and Abnormal Status Based on Video Recordings of Epileptic Patients. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-1049696
Modern Language Association (MLA)
Li, J.…[et al.]. Classifying Normal and Abnormal Status Based on Video Recordings of Epileptic Patients. The Scientific World Journal No. 2014 (2014), pp.1-6.
https://search.emarefa.net/detail/BIM-1049696
American Medical Association (AMA)
Li, J.& Zhen, Xiantong& Liu, Xianzeng& Ouyang, Gaoxiang. Classifying Normal and Abnormal Status Based on Video Recordings of Epileptic Patients. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-1049696
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049696