DocumentCode :
3459609
Title :
Prediction of Human Elbow Torque from EMG Using SVM Based on AWR Information Acquisition Platform
Author :
Song, Quanjun ; Sun, Bingyu ; Lei, Jianhe ; Gao, Zhen ; Yu, Yong ; Liu, Ming ; Ge, Yunjian
Author_Institution :
Inst. of Intell. Machine, Chinese Acad. of Sci., Hefei
fYear :
2006
fDate :
20-23 Aug. 2006
Firstpage :
1274
Lastpage :
1278
Abstract :
In this paper a novel prediction method of elbow torque from EMG signal using SVM is proposed. How to model the relations between EMG signals and various kinematical aspects of the movement behavior is a difficult problem in the researches of neurophysiology and biomechanics. Traditional prediction methods include using neural networks to model the relations. However, these methods suffer from several problems, such as local minima, the difficulty of the selection of the model, etc. To address these problems, support vector machine is adopted to construct the nonlinear model. The efficiency of our proposed method is proved by experiment results.
Keywords :
biomechanics; electromyography; neurophysiology; support vector machines; AWR Information Acquisition Platform; EMG; biomechanics; human elbow torque; neurophysiology; support vector machine; Biomechanics; Elbow; Electromyography; Humans; Neural networks; Neurophysiology; Prediction methods; Predictive models; Support vector machines; Torque; EMG; Information Acquisition; Support Vector Machine; joint Torque;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Acquisition, 2006 IEEE International Conference on
Conference_Location :
Shandong
Print_ISBN :
1-4244-0528-9
Electronic_ISBN :
1-4244-0529-7
Type :
conf
DOI :
10.1109/ICIA.2006.305933
Filename :
4097866
Link To Document :
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