The Joint Application of Rough Set-Based Feature Reduction and Information Fusion in Motion Recognition

摘要：

　　An effective and efficient strategy for motion classification via the electromyographic (EMG) signals is proposed in this paper.The wavelet packet transform (WPT) is utilized to extract the energy characteristics of the sub-bands as the features.Given the redundancy between the features,the rough set theory (RST) is employed to implement the feature reduction.For improving the classification accuracy,the multi-classifier based information fusion strategy is performed to avoid the arbitrary decision of only one classifier with insufficiency information.Compared with the single- classifiers using the same features,more excellent performance indicates the potential of the RST-based feature reduction and the multi-classifier fusion techniques in motion classification.

作者: Zhiguo Yan Zekun Liu

作者单位: Research Center of the Things of Internet The Third Research Institute of Ministry of Public Security Shanghai, China

会议类型: 国际会议

会议名称: 2013 ICME International Conference on Complex Medical Engineering(2013 ICME复合医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 717-722

在线出版日期: 2013-05-25（万方平台首次上网日期，不代表论文的发表时间）