评估肌肉疲劳的肌力图信号分析

S. Okkesim, Kezban Coşkun
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引用次数: 2

摘要

肌肉疲劳是指肌肉收缩时产生力的能力下降。肌肉疲劳是肌肉骨骼问题的危险因素之一,作为一个危险因素;需要从人体工程学和生理学的角度对肌肉疲劳进行定量评价。然而,肌肉疲劳不是一个定量值。因此,研究人员重点研究了几个可测量的参数,如pH值、血氧和乳酸水平,这些参数可以用来分析肌肉疲劳。肌动图(MMG)是定量分析肌肉疲劳时可以记录的信号之一。本研究的目的是用MMG分析等长收缩时的肌肉疲劳。记录了6例自愿受试者(3男3女)肱二头肌和肱三头肌的MMG信号。在时域计算均方根(RMS)值,在频域计算平均频率(MNF)和中位数频率(MDF),用于特征提取。结果表明,从MMG信号可以判断肌肉疲劳程度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of mechanomyogram signals for evaluation of muscle fatigue
Muscle Fatigue is the reduction of force generating capacity during muscle contraction. Muscle fatigue is one of the risk factor for musculoskeletal problems, and as a risk factor; it needs to rating of muscle fatigue quantitatively in the field of ergonomics and physiology. However muscle fatigue is not a quantitative value. Therefore, researchers focus on several measurable parameters which can be used to analyze the muscle fatigue such as, pH, blood oxygen and lactate level. One of the signal which can be recorded for analyzing of muscle fatigue quantitatively is mechanomyography (MMG). The aim of this study is to analyze the muscle fatigue during isometric contractions using MMG. MMG signals were recorded from biceps brachii and triceps brachii muscles of six (3 Female, 3 Male) voluntary subject. For feature extraction purposes, were computed root mean square (RMS) value in time domain, mean frequency (MNF) and median frequency (MDF) in frequency domain. The results showed that muscle fatigue can be evaluated from MMG signals.
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