Muscle fatigue often occurs over a long period of exercise, and it can increase the risk of muscle injury. Evaluating the state of muscle fatigue can avoid unnecessary overtraining and injury of the muscle. Ultrasound imaging can non-invasively visualize muscle tissue in real-time. Image entropy is commonly used to characterize the texture of an image. In this study, we evaluated changes in the ultrasound image entropy (USIE) during the fatigue process. Twelve volunteers performed static sustained contractions of biceps brachii at four different intensities (20%, 30%, 40%, and 50% of maximal voluntary contraction torque). The ultrasound images and surface electromyography (sEMG) signals were acquired during exercise to fatigue. We found that (1) the root-mean-square of the sEMG signal increased, the USIE decreased significantly with time during the sustained contractions; (2) the maximum endurance time (MET) and the decline percentage of USIE were significantly different (p < .05) among the four contraction intensities; (3) the decline slope of USIE of the same volunteer was basically the same at different contraction intensities. The USIE could be a new method for the evaluation of skeletal muscle fatigue state.

中文翻译：

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用超声图像熵评估骨骼肌疲劳状态

肌肉疲劳通常发生在长时间的运动中，它会增加肌肉损伤的风险。评估肌肉疲劳状态可以避免不必要的过度训练和肌肉损伤。超声成像可以非侵入性地实时可视化肌肉组织。图像熵通常用于表征图像的纹理。在这项研究中，我们评估了疲劳过程中超声图像熵 (USIE) 的变化。12 名志愿者以四种不同的强度（最大自主收缩扭矩的 20%、30%、40% 和 50%）对肱二头肌进行静态持续收缩。超声图像和表面肌电图 (sEMG) 信号是在运动至疲劳期间获得的。我们发现（1）sEMG信号的均方根增加，在持续收缩期间，USIE随时间显着下降；(2) 4种收缩强度的最大耐力时间(MET)和USIE下降百分比有显着差异(p < .05)；(3)同一志愿者在不同收缩强度下的USIE下降斜率基本相同。USIE可能是一种评估骨骼肌疲劳状态的新方法。