Pattern recognition based forearm motion classification for patients with chronic hemiparesis
文献类型：会议论文
| 作者 | Geng, Yanjuan; Zhang, Liangqing; Tang, Dan; Zhang, Xiufeng; Li, Guanglin |
| 出版日期 | 2013 |
| 会议名称 | 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 |
| 会议地点 | Osaka, Japan |
| 英文摘要 | To make full use of electromyography (EMG) that contains rich information of muscular activities in active rehabilitation for motor hemiparetic patients, a couple of recent studies have explored the feasibility of applying pattern recognition technique to the classification of multiple motion classes for stroke survivors and reported some promising results. However, it still remains unclear if kinematics signals could also bring good motion classification performance, particularly for patients after traumatic brain damage. In this study, the kinematics signals was used for motion classification analysis in three stroke survivors and two patients after traumatic brain injury, and compared with EMG. The results showed that an average classification error of 7.9±6.8% for the affected arm over all subjects could be achieved with a linear classifier when they performed multiple fine movements, 7.9% lower than that when using EMG. With either kind of signals, the motor control ability of the affected arm degraded significantly in comparison to the intact side. The preliminary results suggested the great promise of kinematics information as well as the biological signals in detecting user's conscious effort for robot-aided active rehabilitation. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/4943]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 作者单位 | 2013 |
| 推荐引用方式 GB/T 7714 | Geng, Yanjuan,Zhang, Liangqing,Tang, Dan,et al. Pattern recognition based forearm motion classification for patients with chronic hemiparesis[C]. 见:2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013. Osaka, Japan. |
入库方式： OAI收割
来源：深圳先进技术研究院
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。