基于实时肌肉疲劳特征融合的表面肌电手势识别增强算法  

Enhancement algorithm for surface electromyographic-based gesture recognition based on real-time fusion of muscle fatigue features

作　　者：严仕嘉 杨晔[1,2] 易鹏 YAN Shijia;YANG Ye;YI Peng(College of Information,Mechanical and Electrical Engineering,Shanghai Normal University,Shanghai 200234,P.R.China;Shanghai Engineering Research Center of Intelligent Education and Bigdata,Shanghai 200234,P.R.China)

机构地区：[1]上海师范大学信息与机电工程学院,上海200234 [2]上海智能教育大数据工程技术研究中心,上海200234

出　　处：《生物医学工程学杂志》2024年第5期958-968,共11页Journal of Biomedical Engineering

摘　　要：本研究旨在优化基于表面肌电图的手势识别技术,重点考虑肌肉疲劳对识别性能的影响。文中提出了一种创新的实时分析算法,可实时提取肌肉疲劳特征,并将其融入手势识别过程中。基于自行采集的数据,本文应用卷积神经网络和长短期记忆网络等算法对肌肉疲劳特征的提取方法进行了深入分析,并对比了肌肉疲劳特征对基于表面肌电图的手势识别任务的性能影响。研究结果显示,通过实时融合肌肉疲劳特征,本文所提出的算法对不同疲劳等级的手势识别准确率均有提升,对于不同个体的平均识别准确率也有提升。综上,本文算法不仅提升了手势识别系统的适应性和鲁棒性,而且其研究过程也可为生物医学工程领域中手势识别技术的发展提供新的见解。This study aims to optimize surface electromyography-based gesture recognition technique,focusing on the impact of muscle fatigue on the recognition performance.An innovative real-time analysis algorithm is proposed in the paper,which can extract muscle fatigue features in real time and fuse them into the hand gesture recognition process.Based on self-collected data,this paper applies algorithms such as convolutional neural networks and long short-term memory networks to provide an in-depth analysis of the feature extraction method of muscle fatigue,and compares the impact of muscle fatigue features on the performance of surface electromyography-based gesture recognition tasks.The results show that by fusing the muscle fatigue features in real time,the algorithm proposed in this paper improves the accuracy of hand gesture recognition at different fatigue levels,and the average recognition accuracy for different subjects is also improved.In summary,the algorithm in this paper not only improves the adaptability and robustness of the hand gesture recognition system,but its research process can also provide new insights into the development of gesture recognition technology in the field of biomedical engineering.

关 键 词：表面肌电信号 肌肉疲劳 手势识别 卷积神经网络 长短期记忆网络 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]