Assessment of Safe Work Indicators in Transportation Construction Using Personal Monitoring Systems

Construction projects require long hours where workers are subjected to intensive tasks such as hard manual labor, heavy lifting, and constrained working postures. Among the physiological metrics, heart rate (HR) is reported to be a good indicator of physical stress and workload. HR forecasting models have been used in various areas including cardiopathy research, heart attack warning indicator, and early physical fatigue detection. However, there are no reported studies on HR modeling and forecasting in the construction field. Modeling and forecasting the HR of construction workers using construction field data is of paramount importance due to the direct relationship between activity level and HR. The objective of this study is to (1) analyze the effect of physiological factors such as breathing rate, acceleration of torso movements, torso posture, and impulse load on the HR of construction workers; and (2) model and forecast one-minute-ahead HR for construction workers based on their physical activity using deep learning algorithms. To this end, physiological metrics of five bridge maintenance workers performing several construction activities were collected. According to the Pearson correlation and entropy based mutual information analysis, time-lagged variables, including acceleration of torso movements, torso posture, and impulse load, have a significant effect on HR data. The results of deep learning models indicate that long short-term memory network (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU) have similar predictive performance. However, LSTM had the best overall performance in HR prediction with mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of 5.4%, 7.34%, and 5.77%, respectively. These models have the potential to facilitate the mitigation of cardiovascular strain and enable proactive prevention of accidents in the construction industry.

• Record URL:
  https://www.ugpti.org/resources/reports/details.php?id=1164
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/74617
• Summary URL:
  https://rosap.ntl.bts.gov/view/dot/74671
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  University of Colorado, Denver

  Department of Civil Engineering
  1200 Larimer Street, P.O. Box 173364
  Denver, CO  United States  80217-3364

  Mountain-Plains Consortium

  North Dakota State University
  Fargo, ND  United States  58108

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Clevenger, Caroline M
  • Abdallah, Moatassem
  • Rens, Kevin
  • Ghafoori, Mahdi
• Publication Date: 2024-3

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; Photos; References; Tables;
• Pagination: 40p

Subject/Index Terms

• TRT Terms: Cardiovascular system; Forecasting; Heart rate; Machine learning; Occupational safety; Road construction workers
• Subject Areas: Construction; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01920232
• Record Type: Publication
• Report/Paper Numbers: MPC-24-520
• Contract Numbers: MPC-649
• Files: UTC, NTL, TRIS, USDOT
• Created Date: May 30 2024 5:13PM