Worker-Centric Heat Strain Analysis: Integrating Physiological Signals with Ensemble Learning and Domain Adaptation

Details

• Personal Author:
  Jebelli H ; Liu Y ; Ojha A ; Sharifironizi A ; Shayesteh S
• Description:
  Despite advances in utilizing physiological sensors and machine learning (ML) algorithms, accurately and consistently monitoring heat strain levels of field workers on job sites remains a challenge not fully addressed by previous research. Existing frameworks often fail to adapt to the diverse physiology of workers and their varied working conditions, leading to concerns about stability, reliability, and accuracy. To address these limitations, a worker-centered heat strain monitoring framework was introduced, leveraging physiological data from wearable biosensors for a more accurate and consistent estimation of heat strain risks. A high-fidelity virtual reality (VR) environment was developed to simulate heat-vulnerable occupations for quality data collection. Building on this foundation, EnsmTrBoost, a physiological sensing framework integrating ensemble learning and domain adaptation, was developed. This framework exhibited a remarkable prediction accuracy exceeding 93%. This paper advances heat strain monitoring and supports the development of early warning systems for heat-related fatalities at work sites. [Description provided by NIOSH]
• Subjects:
  Automation Construction Industry Heat Stress Disorders Hot Temperature Occupational Health Safety
• Keywords:
  Author Keywords: Occupational Heat Strain Risk Biosensors Construction Industry Data-driven Health Monitoring Domain Adaptation Ensemble Learning Heat-vulnerable Occupations Heat Stress Immersive Environments Machine Learning Monitoring Systems Physiological Measurements Virtual Reality

  More +
• ISSN:
  0926-5805
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Chicago Region [OSHA] ; Illinois ; New York ; New York City Region [OSHA] ; Pennsylvania ; Philadelphia Region [OSHA]
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  166
• NIOSHTIC Number:
  nn:20070019
• Citation:
  Autom Constr 2024 Oct; 166:105670
• Contact Point Address:
  Houtan Jebelli, Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States of America
• Email:
  hjebelli@illinois.edu
• Federal Fiscal Year:
  2025
• Performing Organization:
  Pennsylvania State University
• Peer Reviewed:
  True
• Start Date:
  20220901
• Source Full Name:
  Automation in Construction
• End Date:
  20240831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:91bf4d74ce7db3a8539b87a370ad1bc64e63c4c1abca3ba04d67dc245cba4ca19e15801e90df088d946e30633c7064e20afe005d69339ee58e55e07586750101
• Download URL:
  https://stacks.cdc.gov/view/cdc/208325/cdc_208325_DS1.pdf
• File Type:
  [PDF - 9.11 MB ]