Jia Bochen, Kumbhar Abhishek Nagesh, Tong Yourui
Industrial and Manufacturing System Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA.
Somnio Global, LLC., 45145 W 12 Mile Rd., Novi, MI 48377, USA.
Int J Environ Res Public Health. 2021 Oct 26;18(21):11242. doi: 10.3390/ijerph182111242.
Measuring muscle fatigue is one essential and standard method to quantify the ergonomic risks associated with prolonged low-load exposure. However, measuring muscle fatigue using EMG-based methods has shown conflicting results under low-load but sustained work conditions, e.g., prolonged sitting. Muscle stimulation technology provides an alternative way to estimate muscle fatigue development during such work conditions by monitoring the stimulation-evoked muscle responses, which, however, could be restricted by the accessibility and measurability of targeted muscles. This study proposes a computer vision-based method to overcome such potential restrictions by visually quantifying the muscle belly displacement caused by muscle stimulation. The results demonstrate the ability of the developed computer vision-based stimulation method to detect muscle fatigue from prolonged low-load tasks. Current results can be used as a foundation to develop a sensitive and reliable method to quantify the adverse effects of the daily low-load sustained condition in occupational and nonoccupational settings.
测量肌肉疲劳是量化与长时间低负荷暴露相关的人体工程学风险的一种基本且标准的方法。然而，在低负荷但持续的工作条件下，例如长时间坐着，使用基于肌电图的方法测量肌肉疲劳已显示出相互矛盾的结果。肌肉刺激技术提供了一种替代方法，通过监测刺激诱发的肌肉反应来估计在此类工作条件下肌肉疲劳的发展，然而，这可能会受到目标肌肉的可及性和可测量性的限制。本研究提出了一种基于计算机视觉的方法，通过视觉量化肌肉刺激引起的肌腹位移来克服此类潜在限制。结果证明了所开发的基于计算机视觉的刺激方法能够从长时间低负荷任务中检测肌肉疲劳。当前结果可作为开发一种灵敏且可靠的方法的基础，以量化职业和非职业环境中日常低负荷持续状态的不利影响。
