Extreme learning machine classification method for lower limb movement recognition |
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| Authors: | Yuxiang Kuang Qun Wu Junkai Shao Jianfeng Wu Xuehua Wu |
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| Institution: | 1.College of Computer Science and Technology,Zhejiang University,Hangzhou,China;2.School of Art,Jiangxi University of Finance & Economics,Nanchang,China;3.Universal Design Institute,Zhejiang Sci-Tech University,Hangzhou,China;4.Taizhou Research Institute,Zhejiang University,Hangzhou,China;5.Product Design and Reliability Engineering Institute,Southeast University,Nanjing,China;6.School of Art,Zhejiang University of Technology,Hangzhou,China |
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| Abstract: | In order to identify the lower limb movements accurately and quickly, a recognition method based on extreme learning machine (ELM) is proposed. The recognizing target set is constructed by decomposing the daily actions into different segments. To get the recognition accuracy of seven movements based on the surface electromyography, the recognition feature vector space is established by integrating short-time statistical characteristics under time domain, and locally linear embedding algorithm is used to reduce the computational complexity and improve robustness of algorithm. Compared with BP, the overall recognition accuracy for each subject in the best dimension with ELM is above 95%. |
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