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疲劳状态下基于肌肉抽搐模型和表面肌电信号的动态肘关节弯曲力估计。

Dynamic Elbow Flexion Force Estimation Through a Muscle Twitch Model and sEMG in a Fatigue Condition.

出版信息

IEEE Trans Neural Syst Rehabil Eng. 2017 Sep;25(9):1431-1439. doi: 10.1109/TNSRE.2016.2628373. Epub 2016 Nov 14.

Abstract

We propose a joint force estimation method to compute elbow flexion force using surface electromyogram (sEMG) considering time-varying effects in a fatigue condition. Muscle fatigue is a major cause inducing sEMG changes with respect to time over long periods and repetitive contractions. The proposed method composed the muscle-twitch model representing the force generated by a single spike and the spikes extracted from sEMG. In this study, isometric contractions at six different joint angles (10 subjects) and dynamic contractions with constant velocity (six subjects) were performed under non-fatigue and fatigue conditions. Performance of the proposed method was evaluated and compared with that of previous methods using mean absolute value (MAV). The proposed method achieved average 6.7 ± 2.8 %RMSE for isometric contraction and 15.6 ± 24.7%RMSE for isokinetic contraction under fatigue condition with more accurate results than the previous methods.

摘要

我们提出了一种联合力估计方法,利用表面肌电图(sEMG)在疲劳条件下计算肘屈肌力,考虑时变效应。肌肉疲劳是导致 sEMG 随时间长时间和重复收缩而变化的主要原因。所提出的方法由肌肉抽搐模型组成,该模型代表单个抽搐产生的力和从 sEMG 中提取的抽搐。在这项研究中,在非疲劳和疲劳条件下进行了六个不同关节角度的等长收缩(10 个受试者)和恒速的动态收缩(六个受试者)。使用平均绝对值(MAV)评估了所提出方法的性能,并与以前的方法进行了比较。与以前的方法相比,所提出的方法在疲劳条件下的等长收缩时平均达到 6.7 ± 2.8%RMSE,在等速收缩时达到 15.6 ± 24.7%RMSE,结果更准确。

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