摘要
为了提高假肢穿戴者步态识别准确率,提出了一种高阶过零分析技术分析表面肌电信号的假肢步态识别方法。该方法针对假肢穿戴者步态识别过程中的多分类问题,选择表面肌电信号(surface electromyogram signal,简称sEMG)作为步态识别信息源,将表征时间序列特性的高阶过零分析(higher order zero crossing analysis,简称HOC)方法运用于不同步态下的肌电信号的特征提取,结合相关向量机(relevance vector machine,简称RVM)建立了多分类步态识别模型,然后采用蝙蝠算法(bat algorithm,简称BA)对RVM分类器的核函数参数进行优化。实验结果表明,所提方法与粒子群算法优化相关向量机(particle swarm optimization⁃relevance vector machines,简称PSO⁃RVM)及RVM等方法相比,对于平地行走、上楼、下楼、上坡和下坡5种步态的识别准确率均高于PSO⁃RVM和RVM等方法。
In order to improve the gait recognition accuracy of prosthetic wearers,a new method of prosthetic gait recognition based on surface electromyogram signal is proposed.In this method,surface electromyogram signal(sEMG)is selected to solve the multi-classification problem in gait recognition of prosthetic wearers.sEMG is a higher order zero crossing analysis(HOC)square representing the characteristics of time series.The method is applied to feature extraction of EMG signals under different gaits,and multiple classification steps are established by combining with relevance vector machine(RVM).Bat algorithm(BA)is used to optimize kernel function parameters of RVM classifier.Experimental results show that the proposed method is better than parti⁃cle swarm optimization-relevance vector machines(PSO-RVM)and RVM for walking on the ground,upstairs,downstairs,uphill and downhill of five kinds of gaits.The recognition accuracy is higher than PSO-RVM and RVM and other methods.
作者
刘磊
杨鹏
刘作军
宋寅卯
LIU Lei;YANG Peng;LIU Zuojun;SONG Yinmao(School of Building Environmental Engineering,Zhengzhou College of Light Industry Zhengzhou,450002,China;School of Artificial Intelligence and Data Science,Hebei University of Technology Tianjin,300130,China)
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2022年第4期771-776,829,共7页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金资助项目(61803143)
河南省高校科技创新团队资助项目(19IRTSTHN013)
河南省高等学校重点科研资助项目(22B413012)。
关键词
智能假肢
表面肌电信号
相关向量机
蝙蝠算法
步态识别
artificial legs
surface electromyogram signals
relevance vector machine
bat algorithm
gait recog⁃nition
