@article{ref1,
title="A machine learning approach to detect changes in gait parameters following a fatiguing occupational task",
journal="Ergonomics",
year="2018",
author="Baghdadi, Amir and Megahed, Fadel M. and Esfahani, Ehsan T. and Cavuoto, Lora A.",
volume="61",
number="8",
pages="1116-1129",
abstract="The purpose of this study is to provide a method for classifying non-fatigued versus fatigued states following manual material handling. A method of template matching pattern recognition for feature extraction (1$ Recognizer) along with the support vector machine (SVM) model for classification were applied on the kinematics of gait cycles segmented by our stepwise search-based segmentation algorithm. A single inertial measurement unit (IMU) on the ankle was used, providing a minimally intrusive and inexpensive tool for monitoring. The classifier distinguished between states using distance-based scores from the recognizer and the step duration. The results of fatigue detection showed an accuracy of 90% across data from 20 recruited subjects. This method utilizes the minimum amount of data and features from only one low-cost sensor to reliably classify the state of fatigue induced by a realistic manufacturing task using a simple machine learning algorithm that can be extended to real-time fatigue monitoring as a future technology to be employed in the manufacturing facilities. Practitioner Summary We examined the use of a wearable sensor for the detection of fatigue-related changes in gait based on a simulated manual material handling task. Classification based on foot acceleration and position trajectories resulted in 90% accuracy. This method provides a practical framework for predicting realistic levels of fatigue.<p /> <p>Language: en</p>",
language="en",
issn="0014-0139",
doi="10.1080/00140139.2018.1442936",
url="http://dx.doi.org/10.1080/00140139.2018.1442936"
}