[1] Hu Xiao, Wang Zhizhong, Ren Xiaomei,. Classification of forearm action surface EMG signalsbased on fractal dimension [J]. Journal of Southeast University (English Edition), 2005, 21 (3): 324-329. [doi:10.3969/j.issn.1003-7985.2005.03.016]
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Classification of forearm action surface EMG signalsbased on fractal dimension(
)
基于分形维前臂动作表面肌电信号的分类
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 21
- Issue:
- 2005 3
- Page:
- 324-329
- Research Field:
- Biological Science and Medical Engineering
- Publishing date:
- 2005-09-30
Info
- Title:
- Classification of forearm action surface EMG signalsbased on fractal dimension
- 基于分形维前臂动作表面肌电信号的分类
- Author(s):
- Hu Xiao, Wang Zhizhong, Ren Xiaomei
- Department of Biomedical Engineering, Shanghai Jiaotong University, Shanghai 200030, China
- Keywords:
- action surface electromyogram(ASEMG)signal; fractal dimension; wavelet packet transform(WPT); fuzzy self-similarity; Bayes decision
- PACS:
- R318.04
- DOI:
- 10.3969/j.issn.1003-7985.2005.03.016
- Abstract:
- Surface electromyogram(EMG)signals were identified by fractal dimension.Two patterns of surface EMG signals were acquired from 30 healthy volunteers’ right forearm flexor respectively in the process of forearm supination(FS)and forearm pronation(FP).After the raw action surface EMG(ASEMG)signal was decomposed into several sub-signals with wavelet packet transform(WPT), five fractal dimensions were respectively calculated from the raw signal and four sub-signals by the method based on fuzzy self-similarity.The results show that calculated from the sub-signal in the band 0 to 125 Hz, the fractal dimensions of FS ASEMG signals and FP ASEMG signals distributed in two different regions, and its error rate based on Bayes decision was no more than 2.26%.Therefore, the fractal dimension is an appropriate feature by which an FS ASEMG signal is distinguished from an FP ASEMG signal.
- 通过分形维对表面肌电信号进行识别分类.在30个健康志愿者做前臂内旋和外旋时, 从他们的右前臂肌前群分别采集2类动作表面肌电信号.当原始动作表面肌电信号用小波包变换分解成几个子信号后, 采用一种基于模糊自相似性的方法计算原始信号和4个子信号的分形维.结果表明:从频带0~125 Hz的子信号求得的内旋和外旋动作表面肌电信号的分形维有各自的范围;通过该分形维进行Bayes决策时, 错误识别率仅2.26%.因此, 该分形维适合用来识别内旋和外旋动作表面肌电信号.
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Memo
- Memo:
- Biographies: Hu Xiao(1969—), male, PhD;Wang Zhizhong(corresponding author), male, PhD, professor, zzwang@sjtu.edu.cn.