软件导刊2025，Vol.24Issue(5)：8-15,8.DOI:10.11907/rjdk.241947
基于深度学习的表面肌电手势识别研究进展
Research Progress on Surface Electromyography Gesture Recognition Based on Deep Learning
摘要
Abstract
Surface electromyography(sEMG)is a non-invasive bioelectrical signal with rich information about muscle activity,which is wide-ly used in intention recognition studies.With the rapid development of deep learning technology,the accuracy of gesture recognition based on sEMG has been significantly improved,showing a broad application prospect.This review briefly introduces the sEMG gesture recognition pro-cess based on deep learning,focuses on summarizing the research progress and application results of mainstream models such as convolutional neural network,long short-term memory network,Transformer,temporal convolutional network,and generative adversarial network,and looks forward to the future development direction of this field.关键词
表面肌电信号/深度学习/神经网络/手势识别Key words
surface electromyography/deep learning/neural network/gesture recognition分类
计算机与自动化引用本文复制引用
陈怡宁,于益芝..基于深度学习的表面肌电手势识别研究进展[J].软件导刊,2025,24(5):8-15,8.基金项目
国家自然科学基金项目(82472826) （82472826）
