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- Title
An Efficient Method for Identifying Lower Limb Behavior Intentions Based on Surface Electromyography.
- Authors
Liuyi Ling; YiwenWang; FanDing; Li Jin; Bin Feng; Weixiao Li; Chengjun Wang; Xianhua Li
- Abstract
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-basedmethod called EnhancedResidual GateNetwork (ERGN) for lower-limb behavioral intention recognition. The proposed network combines an attentionmechanism and a hard threshold function, while combining the advantages of residual structure, which maps sEMGofmultiple acquisition channels to the lower limbmotion states. Firstly, continuouswavelet 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 ERGNachieves extremely high accuracy and efficiency,with an average recognition accuracy of 98.41% and an average recognition time of only 20ms-outperforming the state-ofthe- art research significantly. Our research provides support for the application of lower limb assisted exoskeleton robots.
- Subjects
ROBOTIC exoskeletons; CONVOLUTIONAL neural networks; SPEECH perception; ELECTROMYOGRAPHY; INTENTION
- Publication
Computers, Materials & Continua, 2023, Vol 77, Issue 3, p2771
- ISSN
1546-2218
- Publication type
Article
- DOI
10.32604/cmc.2023.043383