Classification of Multi-Channels SEMG Signals using Wavelet and Neural Networks on Assistive Robot
Recently, the robot technology research is changing from manufacturing industry to non-manufacturing industry, especially the service industry related to the human life. Assistive robot is a kind of novel service robot. It can not only help the elder and disabled people to rehabilitate their impaired musculoskeletal functions, but also help healthy people to perform tasks requiring large forces. This kind of robot has a broad application prospect in many areas, such as medical rehabilitation, special military operations, special/high intensity physical labour, space, sports, and entertainment. SEMG (Surface Electromyography) of Palmaris longus, brachioradialis, flexor carpiulnaris and biceps brachii are analysed with a wavelet transform method. The absolute variance of 3-layer wavelet coefficients is distilled and regarded as signal characteristics to compose eigenvectors. The eigenvectors are input data of a neural network classifier used to identify 5 different kinds of movement patterns including wrist flexor, wrist extensor, elbow flexion, forearm pronation and forearm rotation. Experiments verify the effectiveness of the proposed method.
surface electromyography wavelet neural network assistive robot
Shuang Gu Yong Yue Carsten Maple Beisheng Liu Chengdong Wu
Department of Computer Science and TechnologyUniversity of BedfordshireLuton, UK School of Information Science and Engineering Northeastern University Shenyang, China
国际会议
IEEE 10th International Conference on Industrial Informatics(第十届IEEE工业信息学国际学术会议 INDIN2012)
北京
英文
1158-1163
2012-07-25（万方平台首次上网日期，不代表论文的发表时间）