Assessment of Construction Workers’ Spontaneous Mental Fatigue Based on Non-Invasive and Multimodal In-Ear EEG Sensors

Abstract

Construction activities are often conducted in outdoor and harsh environments and involve long working hours and physical and mental labor, which can lead to significant mental fatigue among workers. This study introduces a novel and non-invasive method for monitoring and assessing mental fatigue in construction workers. Based on cognitive neuroscience theory, we analyzed the neurophysiological mapping of spontaneous mental fatigue and developed multimodal in-ear sensors specifically designed for construction workers. These sensors enable real-time and continuous integration of neurophysiological signals. A cognitive experiment was conducted to validate the proposed mental fatigue assessment method. Results demonstrated that all selected supervised classification models can accurately identify mental fatigue by using the recorded neurophysiological data, with evaluation metrics exceeding 80%. The long short-term memory model achieved an average accuracy of 92.437%. This study offers a theoretical framework and a practical approach for assessing the mental fatigue of on-site workers and provides a basis for the proactive management of occupational health and safety on construction sites.

Publication DOI: https://doi.org/10.3390/buildings14092793
Divisions: College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing
College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Civil Engineering
Funding Information: The authors acknowledge the support of the Humanities and Social Sciences Fund of the Education Ministry of China (No. 23YJCZH251), the China Postdoctoral Science Foundation (No. 2023M733923), and all the experimental subjects.
Additional Information: Copyright © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Uncontrolled Keywords: cognitive neuroscience,construction safety,deep learning,in-ear sensors,mental fatigue monitoring,Architecture ,Civil and Structural Engineering,Building and Construction
Publication ISSN: 2075-5309
Data Access Statement: Data will be made available on request.
Last Modified: 05 Dec 2024 08:24
Date Deposited: 23 Oct 2024 17:51
Full Text Link: https://www.mdp ... -5309/14/9/2793
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2024-09
Published Online Date: 2024-09-05
Accepted Date: 2024-08-30
Authors: Fang, Xin
Li, Heng
Ma, Jie
Xing, Xuejiao
Fu, Zhibo
Antwi-Afari, Maxwell Fordjour (ORCID Profile 0000-0002-6812-7839)
Umer, Waleed

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