表面肌电关节连续运动估计的研究进展OA北大核心CSTPCD

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 insuffi-cient 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.