An Efficient Method for Identifying Lower Limb Behavior Intentions Based on Surface Electromyography  

作　　者：Liuyi Ling Yiwen Wang Fan Ding Li Jin Bin Feng Weixiao Li Chengjun Wang Xianhua Li 

机构地区：[1]School of Artificial Intelligence,Anhui University of Science and Technology,Huainan,232001,China [2]Institute of Environment-Friendly Materials and Occupational Health,Anhui University of Science and Technology,Wuhu,241003,China [3]School of Electrical&Information Engineering,Anhui University of Science and Technology,Huainan,232001,China [4]School of Mechanical Engineering,Anhui University of Science and Technology,Huainan,232001,China

出　　处：《Computers, Materials & Continua》2023年第12期2771-2790,共20页计算机、材料和连续体（英文）

基　　金：The Research and the Development Fund of the Institute of Environmental Friendly Materials and Occupational Health,Anhui University of Science and Technology,Grant/Award Number:ALW2022YF06;Academic Support Project for Top-Notch Talents in Disciplines(Majors)of Colleges and Universities in Anhui Province,Grant/Award Number:gxbjZD2021052;The University Synergy Innovation Program of Anhui Province,Grant/Award Number:GXXT-2022-053;Anhui Province Key R&D Program of China,Grant/Award Number:2022i01020015.

摘　　要：Surface electromyography(sEMG)is widely used for analyzing and controlling lower limb assisted exoskeleton robots.Behavior intention recognition based on sEMG is of great significance for achieving intelligent prosthetic and exoskeleton control.Achieving highly efficient recognition while improving performance has always been a significant challenge.To address this,we propose an sEMG-based method called Enhanced Residual Gate Network(ERGN)for lower-limb behavioral intention recognition.The proposed network combines an attention mechanism and a hard threshold function,while combining the advantages of residual structure,which maps sEMG of multiple acquisition channels to the lower limb motion states.Firstly,continuous wavelet transform(CWT)is used to extract signals features from the collected sEMG data.Then,a hard threshold function serves as the gate function to enhance signals quality,with an attention mechanism incorporated to improve the ERGN’s performance further.Experimental results demonstrate that the proposed ERGN achieves extremely high accuracy and efficiency,with an average recognition accuracy of 98.41%and an average recognition time of only 20 ms-outperforming the state-of-the-art research significantly.Our research provides support for the application of lower limb assisted exoskeleton robots.

关 键 词：SEMG movement intention efficient network convolutional neural network 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]