Computer Engineering and Applications ›› 2024, Vol. 60 ›› Issue (14): 26-36.DOI: 10.3778/j.issn.1002-8331.2312-0288

• Research Hotspots and Reviews • Previous Articles     Next Articles

Research Progress in Surface Electromyography Joint Continuous Motion Estimation

MA Yifan, WEI Dejian, FENG Yanyan, YU fengfan, LI Zhenjiang   

  1. College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
  • Online:2024-07-15 Published:2024-07-15

表面肌电关节连续运动估计的研究进展

马一凡,魏德健,冯妍妍,于丰帆,李振江   

  1. 山东中医药大学 智能与信息工程学院,济南 250355

Abstract: Surface electromyography (sEMG) is a non-invasive bioelectrical signal used to capture changes in muscle activity during exercise. Because it is closely related to sports, it is widely used in the research and development process of intelligent assisted rehabilitation equipment to provide support and assistance for rehabilitation patients. Rehabilitation training involves complex three-dimensional motion, and sEMG-based joint continuous motion estimation is a method to estimate joint angle or moment by analyzing sEMG during exercise, which can effectively alleviate the problem of insufficient adaptability between rehabilitation machine and human body, providing safer assistance and significantly improving the rehabilitation effect. This paper firstly introduces the current status of joint continuous motion estimation, and then classifies the existing sEMG joint continuous motion estimation models into biomechanics-based musculoskeletal model and machine learning-based regression model according to different research methods, and summarizes and analyzes the relevant models respectively. In addition, the paper also analyzes the current challenges and looks forward to the future research trends.

Key words: surface electromyography signaling (sEMG), continuous movement of joints, musculoskeletal models, regression models

摘要: 表面肌电信号(surface electromyography, sEMG)是一种非侵入式的生物电信号,用于捕捉运动过程中肌肉活动的变化。因其与运动密切相关,所以广泛应用于智能辅助康复设备的研发过程中,为康复者提供支持和帮助。康复训练涉及到复杂的立体运动,而基于sEMG的关节连续运动估计是一种通过分析运动期间的sEMG来估计关节角度或力矩的方法,它能够有效缓解康复机器与人体之间的适应性不足的问题,并提供更安全的辅助,从而显著改善康复效果。介绍了关节连续运动估计的现状,然后根据不同的研究方法将现有的sEMG关节连续运动估计模型分为基于生物力学的肌肉骨骼模型和基于机器学习的回归模型,分别对相关模型进行总结分析;分析了当前所面临的挑战,并展望了未来的研究趋势。

关键词: 表面肌电信号(sEMG), 关节连续运动, 肌肉骨骼模型, 回归模型