title: [Surface electromyography signal classification using gray system theory].
abstract: A new method based on gray correlation was introduced to improve the identification rate in artificial limb. The electromyography (EMG) signal was first transformed into time-frequency domain by wavelet transform. Singular value decomposition (SVD) was then used to extract feature vector from the wavelet coefficient for pattern recognition. The decision was made according to the maximum gray correlation coefficient. Compared with neural network recognition, this robust method has an almost equivalent recognition rate but much lower computation costs and less training samples.
authors.affiliation: Department of Biomedical Engineering, Shanghai Jiaotong University, Shanghai 200030, China. xiehb2008@hotmail.com
authors.forename: Hongbo
authors.initials: H
authors.lastname: Xie
authors.forename: Congbin
authors.initials: C
authors.lastname: Ma
authors.forename: Zhizhong
authors.initials: Z
authors.lastname: Wang
authors.forename: Hai
authors.initials: H
authors.lastname: Huang
mesh_terms: D004576:Electromyography; D006801:Humans; D008954:Models, Biological; D018482:Muscle, Skeletal / Q000502:physiology*; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted; D013598:Systems Theory