Li Jing, Zhen Xiantong, Liu Xianzeng, Ouyang Gaoxiang
Department of Electrical and Automatic Engineering, School of Information Engineering, Nanchang University, Nanchang 330031, China.
Department of Medical Biophysics, University of Western Ontario, Room E5-137, SJHC, 268 Grosvenor Street, London, ON, Canada N6A 4V2.
ScientificWorldJournal. 2014;2014:459636. doi: 10.1155/2014/459636. Epub 2014 Apr 8.
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.
基于癫痫患者运动的视频记录，本文提出了一种人体动作识别方案，以检测不同的运动模式，并区分癫痫患者的正常状态和异常状态。该方案首先提取相互补充的局部特征和整体特征。之后，应用支持向量机进行分类。基于实验结果，该方案获得了令人满意的分类结果，并为在照顾癫痫患者（或其他脑部疾病患者）时使用社会辅助机器人进行人机交互以保护他们免受伤害提供了基础分析。
