title: [Study on the surface EMG pattern classification with BP neural networks].
abstract: This paper presents a surface electromyography (EMG) motion pattern classifier which combines Neural Network (NN) with parametric model such as autoregressive (AR) model. This motion pattern classifier can successfully identify four types of movement of human hand, wrist flexion, wrist extension, forearm pronation and forearm supination, by using of the surface EMG detected from the flexor carpi radialis and the extensor carpi ulnaris. The result shows that it has a great potential application to the control of bionic man-machine systems such as prostheses because of its fast calculating speed, high recognition ability, and good robust.
authors.affiliation: Tsinghua University.
authors.forename: R
authors.initials: R
authors.lastname: Wang
authors.forename: C
authors.initials: C
authors.lastname: Huang
authors.forename: B
authors.initials: B
authors.lastname: Li
authors.forename: D
authors.initials: D
authors.lastname: Jin
authors.forename: J
authors.initials: J
authors.lastname: Zhang
mesh_terms: D001132:Arm; D001186:Artificial Limbs; D004576:Electromyography / Q000145:classification*; D005260:Female; D006801:Humans; D008297:Male; D018482:Muscle, Skeletal / Q000502:physiology; D016571:Neural Networks, Computer; D014953:Wrist