IDENTIFYING HAND-MOTION PATTERNS VIA KERNEL DISCRIMINANT ANALYSIS BASED DIMENSION REDUCTION AND QUADRATIC CLASSIFIER

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.

关键词： Forearm hand-motion Mechanomyography Kernel discriminant analysis Quadratic classifier Rehabilitation engineering

作者: WEI CAO YONG ZENG CHUN-MING XIA HENG CAO

作者单位: Department of Mechauical Engineering, East China University of Science and Technology, Shanghai, 200237, P.R.China

会议类型: 国际会议

会议名称: 2011 International Conference on Wavelet Analysis and Pattern Recognition(2011小波分析与模式识别国际会议)

会议地点: 桂林

会议语种:英文

页码: 1-6

在线出版日期: 2011-07-10（万方平台首次上网日期，不代表论文的发表时间）