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In Ergonomics

Human muscle fatigue is the main result of diminishing muscle capability, leading to reduced performance and increased risk of falls and injury. This study provides a classification model to identify the human fatigue level based on the motion signals collected by a smartphone. Twenty-four participants were recruited and performed the fatiguing exercise (i.e., squatting). Upon completing each set of squatting, they walked for a fixed distance while the smartphone attached to their right shank and the gait data were associated to the Borg's Rating of Perceived Exertion (i.e., data label). Our machine-learning model of two (no- vs. strong-fatigue), three (no-, medium-, and strong-fatigue) and four (no-, low-, medium-, and strong-fatigue) levels of fatigue reached to the accuracy of 91%, 78%, and 64%, respectively. The outcomes of this study may facilitate the accessibility of a fatigue-monitoring tool in workplace, which improves the workers' performance and reduce the risk of falls and injury. PRACTITIONAR SUMMARY: This study aimed to develop a machine-learning model to identify human fatigue level using motion data captured by a smartphone attached to the shank. Our results can facilitate the development of an accessible fatigue-monitoring system that may improve the workers' performance and reduce the risk of falls and injury.

Karvekar Swapnali, Abdollahi Masoud, Rashedi Ehsan

2021-Jan-04

Human Muscle fatigue, Machine Learning, Pattern Recognition, Smartphone, Support Vector Machine, Wearable Technology