title: [On selecting typical samples in EMG pattern classification].
abstract: As is well known that the quality of training samples directly influence the recognizing ability of neural network. In this paper, we introduce a method for solving the problem of how to classify the pattern of forearm by obtaining typical samples. At first, the original samples were pretreated by using the membership class function that can improve the quality of cluster sample. Then, the center of clustering could be gained by using the method of clustering and the typical sample was obtained. Based on this method, we can get the typical sample that corresponds with the movements of stretch of arm and fold of arm. We can make them as the training sample of the BP network to classify the pattern of forearm. The experiment indicates that this measure can improve the point of identification.
authors.affiliation: Intelligence & Robotics Institute, University of Electronic Science and Technology of Hangzhou, Hangzhou 310018, China. luo@hziee.edu.cn
authors.forename: Zhizeng
authors.initials: Z
authors.lastname: Luo
authors.forename: Fei
authors.initials: F
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D004576:Electromyography / Q000379:methods*; D005542:Forearm / Q000502:physiology*; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted