表面肌电手部动作识别的研究进展

doi:10.3778/j.issn.1002-8331.2305-0269

摘要/Abstract

摘要： 表面肌电信号（surface electromyography，sEMG）是一种测量肌肉活动的非侵入式方法，蕴含着关联人体运动的丰富信息，可用于手部动作识别。基于sEMG手部动作识别是指通过分析手部肌肉的sEMG信号，实现对手部动作的分类和识别。在神经网络发展的推动下，sEMG在手部动作识别领域取得了显著进展，但sEMG面临着噪声大、稳定性差等缺陷，难以有效利用，给高精度手部动作识别模型的获取带来了巨大困难，阻碍了研究成果的转化应用。详细归纳了sEMG手部动作识别方法的研究进展；介绍了常用于动作识别领域的公开肌电数据集，并介绍了自测肌电数据集采集流程；根据研究方法不同将现有的sEMG手部动作识别模型分为基于机器学习的手部动作识别、基于深度学习的手部动作识别和基于混合网络结构的手部动作识别三类，分别对相关模型进行总结分析，对不足之处提出建议；最后对手部动作识别研究需要解决的问题和未来发展方向进行了展望。

关键词: 表面肌电信号（sEMG）, 手部动作识别, 人工神经网络, 算法模型

Abstract: Surface electromyography (sEMG) is a non-invasive method of measuring muscle activity, which contains rich information related to human motion and can be used for hand motion recognition. Hand motion recognition based on sEMG refers to the classification and recognition of hand motions by analyzing the sEMG signals of the hand muscles. Driven by the development of neural networks, sEMG has made great progress in the field of hand motion recognition. However, sEMG is faced with defects such as high noise and poor stability, which cannot be efficiently utilised, bringing great difficulties in acquiring high-precision hand movement recognition models and has hindered the translation and application of research results. This paper summarizes the research progress of sEMG hand motion recognition methods in detail. Firstly, public EMG datasets commonly used in the field of action recognition are introduced, and the self-test EMG set acquisition process is described. Then the existing sEMG hand motion recognition models are classified into three categories according to the different research methods: hand motion recognition based on machine learning, hand motion recognition based on deep learning and hand motion recognition based on hybrid network structure, and the related models are summarised and analyzed respectively, and suggestions are made for the shortcomings. Finally, the problems to be solved and the future development direction of hand action recognition research are prospected.

Key words: surface electromyography (sEMG), hand motion recognition, artificial neural network, algorithm model

李振江, 魏德健, 冯妍妍, 于丰帆, 马一凡. 表面肌电手部动作识别的研究进展[J]. 计算机工程与应用, 2024, 60(3): 29-43.

LI Zhenjiang, WEI Dejian, FENG Yanyan, YU Fengfan, MA Yifan. Research Progress of Surface Electromyography Hand Motion Recognition[J]. Computer Engineering and Applications, 2024, 60(3): 29-43.

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