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在等速肌肉疲劳运动过程中表面肌电信号的时频分析。

Time-frequency analysis of surface electromyographic signals during fatiguing isokinetic muscle actions.

机构信息

Department of Health and Exercise Science, University of Oklahoma, Norman, Oklahoma, USA.

出版信息

J Strength Cond Res. 2012 Jul;26(7):1904-14. doi: 10.1519/JSC.0b013e318239c1e6.

Abstract

The purpose of this study was to use a wavelet analysis designed specifically for electromyography (EMG) signals in combination with a trend plot to examine changes in EMG intensity patterns during maximal, fatiguing isokinetic muscle actions. Eleven men (mean ± SD age = 22.4 ± 1.1 years) and 7 women (mean ± SD age = 22.7 ± 2.1 years) performed 50 consecutive maximal concentric isokinetic muscle actions of the dominant leg extensors at a velocity of 180°·s(-1). During each muscle action, a bipolar surface EMG signal was detected from the vastus lateralis. All signals were then processed with a wavelet analysis designed specifically for EMG signals, which resulted in EMG intensity patterns. The patterns for each subject were then analyzed with a trend plot, which provided information regarding the changes that occurred because of fatigue. The results indicated that for all the 18 subjects, the EMG intensity patterns moved in a predictable manner in pattern space, but the changes to the patterns were different for each subject. These findings reflect the complex changes that occur in the EMG signal during fatigue. These changes cannot be characterized fully with a single amplitude and center frequency parameter and can be useful for athletes and coaches who need to track the fatigue status of individual muscles.

摘要

本研究旨在使用专门针对肌电图(EMG)信号设计的小波分析,并结合趋势图,来检查在最大疲劳等速肌肉动作过程中 EMG 强度模式的变化。11 名男性(平均±SD 年龄=22.4±1.1 岁)和 7 名女性(平均±SD 年龄=22.7±2.1 岁)以 180°·s(-1)的速度进行了 50 次连续的主导腿伸肌最大向心等速肌肉动作。在每次肌肉动作中,从股外侧肌检测到双极表面 EMG 信号。然后,所有信号都经过专门针对 EMG 信号设计的小波分析进行处理,从而产生 EMG 强度模式。然后,对每个受试者的模式进行趋势图分析,提供因疲劳而发生的变化的信息。结果表明,对于所有 18 名受试者,EMG 强度模式在模式空间中以可预测的方式移动,但每个受试者的模式变化都不同。这些发现反映了疲劳过程中 EMG 信号发生的复杂变化。这些变化不能仅用单一的幅度和中心频率参数来充分描述,对于需要跟踪个体肌肉疲劳状态的运动员和教练来说可能很有用。

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