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识别骑行过程中因肌肉疲劳导致的表面肌电图变化的策略。

Strategies to identify changes in SEMG due to muscle fatigue during cycling.

作者信息

Singh V P, Kumar D K, Polus B, Fraser S

机构信息

Electrical and Computer Eng, RMIT University, Melbourne, VIC 3000, Australia.

出版信息

J Med Eng Technol. 2007 Mar-Apr;31(2):144-51. doi: 10.1080/03091900500444281.

Abstract

Detection, quantification and analysis of muscle fatigue are crucial in occupational/rehabilitation and sporting settings. Sports organizations such as the Australian Institute of Sports (AIS) currently monitor fatigue by a battery of tests including invasive techniques that require taking blood samples and/or muscle biopsies, the latter of which is highly invasive, painful, time consuming and expensive. SEMG is non-invasive monitoring of muscle activation and is an indication of localized muscle fatigue based on the observed shift of the power spectral density of the SEMG. But the success of SEMG based techniques is currently limited to isometric contraction and is not acceptable to the human movement community. This paper proposes and tests the use of spectral analysis of narrow windows of SEMG near the peak of a cyclic activity to identify the onset of muscle fatigue during cyclic activities. The results demonstrate a highly significant relationship of reduction of the median frequency with the onset of muscle fatigue. The paper also reports the validation of the SEMG study using biochemical analysis of muscle biopsy and blood tests and further verified using power output of the cycle and speed of pedalling.

摘要

在职业/康复和体育领域,肌肉疲劳的检测、量化和分析至关重要。诸如澳大利亚体育学院(AIS)这样的体育组织目前通过一系列测试来监测疲劳,这些测试包括需要采集血样和/或进行肌肉活检的侵入性技术,其中后者具有高度侵入性、痛苦、耗时且昂贵。表面肌电图(SEMG)是对肌肉激活的非侵入性监测,并且基于观察到的SEMG功率谱密度的变化来指示局部肌肉疲劳。但是基于SEMG的技术目前仅成功应用于等长收缩,并且未被人体运动领域所接受。本文提出并测试了在周期性活动峰值附近对SEMG窄窗口进行频谱分析,以识别周期性活动期间肌肉疲劳的起始。结果表明,中频降低与肌肉疲劳起始之间存在高度显著的关系。本文还报告了使用肌肉活检的生化分析和血液测试对SEMG研究进行的验证,并通过自行车功率输出和踏频进一步验证。

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