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 prosthe...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.展开更多
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:ALW2022YF06Academic Support Project for Top-Notch Talents in Disciplines(Majors)of Colleges and Universities in Anhui Province,Grant/Award Number:gxbjZD2021052+1 种基金The University Synergy Innovation Program of Anhui Province,Grant/Award Number:GXXT-2022-053Anhui Province Key R&D Program of China,Grant/Award Number:2022i01020015.
文摘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.
