机器人2017，Vol.39Issue(5)：661-669,9.DOI:10.13973/j.cnki.robot.2017.0661
采用核主成分分析和相关向量机的人体运动意图识别
Human Motion Intent Recognition Based on Kernel Principal Component Analysis and Relevance Vector Machine
摘要
Abstract
For the low recognition rate of human motion intent, a human gait recognition method combining kernel princi-pal component analysis (KPCA) and relevance vector machine (RVM) is proposed. The surface electromyography (sEMG) is selected as gait recognition information source, whose wavelet packet energy is extracted as characteristic value. The KPCA method is adopted to reduce the dimension of characteristic values for removing redundant information, so as to obtain the characteristic values which can reflect the human gait characteristics. Finally, the gait characteristic vectors are classified by RVM to recognize upslope, downslope, stairs ascent, stairs descent or level-ground walking. The feasibility and practicability of the method are verified through analyzing the gait recognition results of different subjects. Compared with BP (backpropa-gation) neural network and SVM (support vector machine) methods, the classification time of the proposed method is 2.6609 × 10?4 s, and the recognition accuracy is 96.67%, which demonstrate it is an effective gait recognition method.关键词
表面肌电信号/核主成分分析/相关向量机/运动意图识别Key words
surface electromyography (sEMG)/kernel principal component analysis (KPCA)/relevance vector machine (RVM)/motion intent recognition分类
信息技术与安全科学引用本文复制引用
刘磊,杨鹏,刘作军,宋寅卯..采用核主成分分析和相关向量机的人体运动意图识别[J].机器人,2017,39(5):661-669,9.基金项目
国家自然科学基金(61203323) （61203323）
河南省高等学校重点科研项目(16B413006) （16B413006）
河北省自然科学基金(F2015202150,F2017202119) （F2015202150,F2017202119）
河南省科技厅重点科研项目(162300410070). （162300410070）
