信息与控制2024，Vol.53Issue(6)：761-773,13.DOI:10.13976/j.cnki.xk.2024.3301
基于肌压信号与惯性测量单元的下肢运动意图识别
Recognition of Lower Limb Movement Intention Based on Force Myography Signal and Inertial Measurement Unit
摘要
Abstract
Accurate recognition of the lower limb movement intentions of amputees is the key to impro-ving the human-machine interaction performance of lower limb prosthetics and reducing the energy consumption of prosthetic users.We use a self-designed signal acquisition system to collect lower limb kinematic signals from femoral amputees and healthy subjects in seven different gait modes,including thigh residual force myography signal(FMG)signals and signals such as leg angle and acceleration from the six degrees of freedom inertial measurement unit(IMU).In addition,the time-domain features of the two signals are fused using a feature fusion method.Machine learn-ing methods are used to investigate the accuracy of lower limb gait pattern classification under the fusion of different information sources.The research results show that by using the three classifica-tion algorithms,the average classification accuracy of FMG-IMU signals for amputees and healthy subjects increase by4.7%and9.5%,respectively,compared with the average classification accu-racy of single FMG signal and single kinematic signal training models,with a maximum average classification accuracy of 99.6%.These results indicate that the lower limb motion intention recog-nition scheme based on the fusion of FMG and IMU signals can achieve good results and is expec-ted to further expand research on FMG signals in the field of lower limb motion intention recogni-tion.Furthermore,these findings provide theoretical support and practical guidance for the further improvement and optimization of lower limb prostheses.关键词
肌压信号/惯性测量单元/股骨截肢者/信号融合/下肢运动意图识别Key words
force myography signal/inertial measurement unit/femoral amputee/signal fusion/lower limb movement intention recognition分类
信息技术与安全科学引用本文复制引用
杜妍辰,杨良栋,汪晓铭,黎林荣,廖文祥,喻洪流..基于肌压信号与惯性测量单元的下肢运动意图识别[J].信息与控制,2024,53(6):761-773,13.基金项目
国家自然科学基金项目(62073224) （62073224）
国家重点研发计划(2018YFB1307303) （2018YFB1307303）
