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