Evaluation of Data Processing and Artifact Removal Approaches Used for Physiological Signals Captured Using Wearable Sensing Devices during Construction Tasks

Abstract

Wearable sensing devices (WSDs) have enormous promise for monitoring construction worker safety. They can track workers and send safety-related information in real time, allowing for more effective and preventative decision making. WSDs are particularly useful on construction sites since they can track workers’ health, safety, and activity levels, among other metrics that could help optimize their daily tasks. WSDs may also assist workers in recognizing health-related safety risks (such as physical fatigue) and taking appropriate action to mitigate them. The data produced by these WSDs, however, is highly noisy and contaminated with artifacts that could have been introduced by the surroundings, the experimental apparatus, or the subject’s physiological state. These artifacts are very strong and frequently found during field experiments. So, when there is a lot of artifacts, the signal quality drops. Recently, artifacts removal has been greatly enhanced by developments in signal processing, which has vastly enhanced the performance. Thus, the proposed review aimed to provide an in-depth analysis of the approaches currently used to analyze data and remove artifacts from physiological signals obtained via WSDs during construction-related tasks. First, this study provides an overview of the physiological signals that are likely to be recorded from construction workers to monitor their health and safety. Second, this review identifies the most prevalent artifacts that have the most detrimental effect on the utility of the signals. Third, a comprehensive review of existing artifact-removal approaches were presented. Fourth, each identified artifact detection and removal approach was analyzed for its strengths and weaknesses. Finally, in conclusion, this review provides a few suggestions for future research for improving the quality of captured physiological signals for monitoring the health and safety of construction workers using artifact removal approaches.

Publication DOI: https://doi.org/10.1061/jcemd4.coeng-13263
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Civil Engineering
College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing
Additional Information: Copyright © 2023 American Society of Civil Engineers. This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers. This material may be found at: https://doi.org/10.1061/JCEMD4.COENG-13263
Uncontrolled Keywords: Artifact eradication,Construction health,Construction safety,Digital construction,Noise removal,Physiological signals,Sensing devices,Industrial relations,Building and Construction,Civil and Structural Engineering,Strategy and Management
Publication ISSN: 1943-7862
Last Modified: 11 Dec 2024 08:22
Date Deposited: 15 Nov 2023 17:24
Full Text Link:
Related URLs: https://ascelib ... MD4.COENG-13263 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Review article
Published Date: 2024-01-01
Published Online Date: 2023-10-25
Accepted Date: 2023-10-01
Authors: Anwer, Shahnawaz
Li, Heng
Antwi-Afari, Maxwell Fordjour (ORCID Profile 0000-0002-6812-7839)
Mirza, Aquil Maud
Rahman, Mohammed Abdul
Mehmood, Imran
Guo, Runhao
Wong, Arnold Yu Lok

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