Citation

Baghdadi A, Megahed FM, Esfahani ET, Cavuoto LA. Ergonomics 2018; 61(8): 1116-1129.

Affiliation

a Department of Industrial and Systems Engineering , University at Buffalo, The State University of New York , Buffalo , NY 14260 , USA.

Copyright

(Copyright © 2018, Informa - Taylor and Francis Group)

DOI

10.1080/00140139.2018.1442936

PMID

29452575

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.

Language: en

Keywords

classification; inertial measurement unit (IMU); physical fatigue; wearable sensors