文献类型：期刊文献

英文题名：Pattern recognition of finger-motions based on diffusion maps and fuzzy K-nearest Neighbor classifier

作者：Song, Zhongjian[1]; Wu, Qing[1]; Xia, Chunming[1]

机构：[1] Department of Mechanical Engineering, East China University of Science and Technology, Shanghai, 200237, China

年份：2012

卷号：2

起止页码：1207

外文期刊名：International Conference on Signal Processing Proceedings, ICSP

收录：EI(收录号：20131716243043)

语种：英文

外文关键词：Classification (of information) - Degrees of freedom (mechanics) - Palmprint recognition - Prosthetics - Motion compensation - Time and motion study - Biomedical signal processing - Nearest neighbor search - Diffusion

摘要：Mechanomyographic (MMG) signal used for prosthetic hands has aroused the interest of a growing number of scholars in recent years and some considerable results have been achieved, however most MMG based approaches are limited to hand movements identification and control. In order to achieve a higher degree of freedom in hand movements, this paper proposed a novel method aiming at identifying the finger-motion patterns. Four-channel MMG signal was adopted to identify six single and combined finger-motion patterns. A total of 50 time-domain and frequency-domain features were extracted and diffusion maps were utilized to reduce the dimension of feature space. The fuzzy K-Nearest Neighbor (f-KNN) classifier was used to identify the six finger-motion patterns. The results showed that the average identification rate reaches to a high accuracy of 95.48 2.47%, which indicates that this algorithm is feasible and effective to identify the six finger-motion patterns and the MMG signal is a prospective alternative in the control of high freedom prosthetic hand. ? 2012 IEEE.