A machine learning approach to detect changes in gait parameters following a fatiguing occupational task. Issue 8 (3rd August 2018)

• Record Type: Journal Article
• Authors: Baghdadi, Amir; Megahed, Fadel M.; Esfahani, Ehsan T.; Cavuoto, Lora A.
• Is Part Of: Ergonomics. Volume 61:Issue 8(2018)
• Page Start: 1116
• Page End: 1129
• Publication Date: 2018-08-03
• Subjects: Inertial measurement unit (IMU) -- classification -- physical fatigue -- wearable sensors; Human engineering -- Periodicals; Cybernetics -- Periodicals; Industrial management -- Periodicals; Ergonomie -- Périodiques; Cybernétique -- Périodiques; Gestion d'entreprise -- Périodiques; 620.8205
• DOI: 10.1080/00140139.2018.1442936
• Languages: English
• ISSNs: 0014-0139

Abstract

Abstract: The purpose of this study is to provide a method for classifying non - fatigued vs. fatigued states following manual material handling. A method of template matching pattern recognition for feature extraction ($1 Recognizer) along with the support vector machine model for classification were applied on the kinematics of gait cycles segmented by our stepwise search-based segmentation algorithm. A single inertial measurement unit 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 recogniser and the step duration. The results of fatigue detection showed an accuracy of 90% across data from 20 recruited subjects. This method utilises 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.