Development of a Computer Vision-Based Muscle Stimulation Method for Measuring Muscle Fatigue during Prolonged Low-Load Exposure

Bochen Jia, Abhishek Nagesh Kumbhar and Yourui Tong
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Bochen Jia: Industrial and Manufacturing System Engineering, University of Michigan—Dearborn, Dearborn, MI 48128, USA
Abhishek Nagesh Kumbhar: Somnio Global, LLC., 45145 W 12 Mile Rd., Novi, MI 48377, USA
Yourui Tong: Industrial and Manufacturing System Engineering, University of Michigan—Dearborn, Dearborn, MI 48128, USA

IJERPH, 2021, vol. 18, issue 21, 1-11

Abstract: 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.

Keywords: muscle fatigue; computer vision; muscle stimulation; low-load exposure; ergonomics; prolonged exposure (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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