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作 者:杨旭升[1,2] 王雪儿 汪鹏君 张文安 YANG Xu-Sheng;WANG Xue-Er;WANG Peng-Jun;ZHANG Wen-An(College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023;Zhejiang Provincial United Key Laboratory of Embedded Systems,Hangzhou 310023;College of Electrical and Electronic Engineering,Wenzhou University,Wenzhou 325035)
机构地区:[1]浙江工业大学信息工程学院,杭州310023 [2]浙江省嵌入式系统联合重点实验室,杭州310023 [3]温州大学电气与电子工程学院,温州325035
出 处:《自动化学报》2023年第8期1723-1731,共9页Acta Automatica Sinica
基 金:浙江省“尖兵”“领雁”研发攻关计划(2022C03114);国家自然科学基金(62173305);浙江省自然科学基金(LD21F030002)资助。
摘 要:针对基于表面肌电信号(Surface electromyography, sEMG)的人体肢体运动估计建模困难的问题,提出一种渐进无迹卡尔曼滤波网络(Progressive unscented Kalman filter network, PUKF-net),来实现降低肢体运动与sEMG量测的建模难度以及提高肢体运动估计精度的目的.首先,设计深度神经网络从sEMG数据中学习肢体运动状态与sEMG量测之间的映射关系和噪声统计特性.其次,采用渐进量测更新方法对先验状态估计进行修正,减小运动估计的线性化误差,提高PUKF-net模型的稳定性.通过结合深度神经网络和渐进卡尔曼滤波的优势,使得PUKF-net具有良好的模型适应性和抗噪能力.最后,设计基于sEMG的人体肢体运动估计实验,验证了PUKF-net模型的有效性.相较于长短期记忆网络(Long short-term memory, LSTM)和其他卡尔曼滤波网络, PUKF-net在肢体运动估计中的均方根误差(Root mean square error, RMSE)下降了14.9%,相关系数R2提高了5.1%.To solve the difficult modeling problem of human limb motion estimation based on surface electromyography(sEMG),a progressive unscented Kalman filter network(PUKF-net)is proposed to reduce the difficulty of modeling limb motion and sEMG measurements and improve the accuracy of limb motion estimation.Firstly,a deep neural network is designed to learn the mapping relationship between limb motion states and sEMG measurements and the statistical property of noise from sEMG data.Secondly,a progressive measurement update method is used to correct the priori state estimate for reducing the linearization error of motion estimation and improving the stability of the PUKF-net.By combining the advantages of deep neural network and progressive Kalman filter,the PUKF-net has good model adaptability and anti-noise capability.Finally,a human limb motion estimation experiment based on sEMG is designed to verify the validity of the PUKF-net.Compared with the long short-term memory(LSTM)and other Kalman filter network models,the root mean square error(RMSE)of PUKFnet in limb motion estimation has decreased by 14.9%and the correlation coefficient R2 has increased by 5.1%.
关 键 词:卡尔曼滤波网络 人体肢体运动估计 表面肌电信号 渐进无迹卡尔曼滤波
分 类 号:R318[医药卫生—生物医学工程] TN911.7[医药卫生—基础医学] TN713[电子电信—通信与信息系统]
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