文献类型：会议论文

英文题名：Hand-motion Patterns Recognition based on Mechanomyographic Signal Analysis

作者：Zeng, Yong[1];Yang, Zhengyi[1];Cao, Wei[1];Xia, Chunming[1]

机构：[1]E China Univ Sci & Technol, Dept Mech Engn, Shanghai 200237, Peoples R China

会议论文集：International Conference on Future BioMedical Information Engineering

会议日期：DEC 13-14, 2009

会议地点：Sanya, PEOPLES R CHINA

语种：英文

外文关键词：hand-motion; Mechanomyography; principal component analysis; quadratic classifier

摘要：A Mechanomyography (MMG) based hand-motion patterns recognition approach was proposed in this paper. With the MMG signal acquired in the upper arm via a single sensor, eleven original features were extracted, and they were further processed by principal components analysis (PCA) in order to reduce the dimension of the feature space. Quadratic discriminant analysis (QDA) was used for four hand-motion patterns recognition. The cross-validated experimental results show that PCA method is practical in dimension reduction and QDA is functional in classifying the four types of hand-motion modes. The average classification accuracy of eight subjects is 79.66%+/- 7.32%. It also reveals that MMG signal is effective in classifying more than two hand-motion patterns even with only one channel signal, and can provide a new choice of control signal for upper-limb prosthetic hand design.