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.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2024 Oct 25;41(5):958-968. doi: 10.7507/1001-5515.202312023.
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.
本研究旨在优化基于表面肌电图的手势识别技术，重点关注肌肉疲劳对识别性能的影响。本文提出了一种创新的实时分析算法，该算法可以实时提取肌肉疲劳特征并将其融合到手部手势识别过程中。基于自行收集的数据，本文应用卷积神经网络和长短期记忆网络等算法，对肌肉疲劳的特征提取方法进行深入分析，并比较肌肉疲劳特征对基于表面肌电图的手势识别任务性能的影响。结果表明，通过实时融合肌肉疲劳特征，本文提出的算法提高了不同疲劳水平下手部手势识别的准确率，不同受试者的平均识别准确率也有所提高。综上所述，本文算法不仅提高了手势识别系统的适应性和鲁棒性，其研究过程还可为生物医学工程领域手势识别技术的发展提供新的见解。
