@article{ref1,
title="Smartphone-based human fatigue level detection using machine learning approaches",
journal="Ergonomics",
year="2021",
author="Rashedi, Ehsan and Abdollahi, Masoud and Karvekar, Swapnali",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="0014-0139",
doi="10.1080/00140139.2020.1858185",
url="http://dx.doi.org/10.1080/00140139.2020.1858185"
}