使用肌电图分析不同MVC水平年轻人的肌肉疲劳

A. Zaman, T. Sharmin, Mohammad Ali Khan, M. Ferdjallah
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引用次数: 14

摘要

频谱参数如平均频率和中位数频率已被证明是评估肌肉疲劳的可靠结果变量。本文的目的是检查年轻受试者在不同水平的最大自愿收缩(MVC)的表面肌电图(EMG)信号,并分析平均和中位数频率(MNF, MDF)的频谱移位。在这项研究中,来自下肢肌肉的肌电数据集的连续流被用来表征从最小到最大MVC水平的肌肉疲劳。在恒定扭矩水平下,使用自动测功机记录肱二头肌等距收缩时的肌电图数据。肌电指标如MNF、MDF、均方根(RMS)和校正均方根(RRMS)被计算来评估肌肉疲劳模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Muscle fatigue analysis in young adults at different MVC levels using EMG metrics
Spectral parameters such as the mean and median frequencies have been documented to be reliable outcome variables for the assessment of muscle fatigue. The objective of this paper is to examine young subjects' surface electromyography (EMG) signals at different levels of maximum voluntary contractions (MVC) and to analyze spectral shifts in mean and median frequencies (MNF, MDF). In this study, continuous stream of EMG data sets from lower extremity muscles are used to characterize muscular fatigue from minimum to maximum MVC level. EMG data were recorded from the biceps brachii muscles during isometric contraction at constant torque levels using automated dynamometer. EMG metrics such as MNF, MDF, root mean square (RMS), and rectified root mean square (RRMS) were computed to assess muscle fatigue patterns.
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