文献类型：期刊文献

英文题名：Identifying hand-motion patterns via kernel discriminant analysis based dimension reduction and quadratic classifier

作者：Cao, Wei[1]; Zeng, Yong[1]; Xia, Chun-Ming[1]; Cao, Heng[1]

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

年份：2011

起止页码：1

外文期刊名：International Conference on Wavelet Analysis and Pattern Recognition

收录：EI(收录号：20114514486659)

语种：英文

外文关键词：Time and motion study - Biomedical signal processing - Motion analysis - Palmprint recognition - Prosthetics

摘要：Mechanomyographic (MMG) signal for prosthetic control has been investigated in recent years and encouraging results in hand-motion patterns identification have been achieved. In this paper, only two accelerometer sensors were used to record the MMG signal in the forearm of fourteen able-bodied people. A kernel generalized discriminant analysis and three linear dimension reduction techniques were applied to reduce the feature dimensionality and improve the class seperability, and then the simple and commonly used quadratic classifier was implemented to identify the four hand-motion patterns. The experimental results have shown that the average identification rate reaches to a high accuracy of 95.12±3.83% by utilizing the three features extracted by kernel generalized discriminant analysis, where two wrist-related patterns are easier to identify while hand close is the most difficult one. It is concluded that two-channel MMG signal is sufficient for identifying the recorded four hand-motion patterns, which made MMG signal another prospective alternative in prosthetic hand control applications. ? 2011 IEEE.