Soo Yewguan, Sugi Masao, Nishino Masataka, Yokoi Hiroshi, Arai Tamio, Kato Ryu, Nakamura Tatsuhiro, Ota Jun
Department of Precision Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan 113-8656.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2975-8. doi: 10.1109/IEMBS.2009.5332521.
Muscle fatigue is commonly associated with the musculoskeletal disorder problem. Previously, various techniques were proposed to index the muscle fatigue from electromyography signal. However, quantitative measurement is still difficult to achieve. This study aimed at proposing a method to estimate the degree of muscle fatigue quantitatively. A fatigue model was first constructed using handgrip dynamometer by conducting a series of static contraction tasks. Then the degree muscle fatigue can be estimated from electromyography signal with reasonable accuracy. The error of the estimated muscle fatigue was less than 10% MVC and no significant difference was found between the estimated value and the one measured using force sensor. Although the results were promising, there were still some limitations that need to be overcome in future study.
肌肉疲劳通常与肌肉骨骼疾病问题相关。此前，人们提出了各种技术来从肌电图信号中对肌肉疲劳进行指数化。然而，定量测量仍然难以实现。本研究旨在提出一种定量估计肌肉疲劳程度的方法。首先通过进行一系列静态收缩任务，使用握力计构建了一个疲劳模型。然后，可以从肌电图信号中以合理的精度估计肌肉疲劳程度。估计的肌肉疲劳误差小于10%最大自主收缩，并且估计值与使用力传感器测量的值之间没有发现显著差异。尽管结果很有前景，但在未来的研究中仍有一些局限性需要克服。
