Author
Listed:
- 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)
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.
Suggested Citation
Bochen Jia & Abhishek Nagesh Kumbhar & Yourui Tong, 2021.
"Development of a Computer Vision-Based Muscle Stimulation Method for Measuring Muscle Fatigue during Prolonged Low-Load Exposure,"
IJERPH, MDPI, vol. 18(21), pages 1-11, October.
Handle:
RePEc:gam:jijerp:v:18:y:2021:i:21:p:11242-:d:665104
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