Autoencoder-Based Photoplethysmography (PPG) Signal Reliability Enhancement in Construction Health Monitoring

Details

• Personal Author:
  Gautam Y ; Jebelli H
• Description:
  Prior research has validated Photoplethysmography (PPG) as a promising biomarker for assessing stress factors in construction workers, including physical fatigue, mental stress, and heat stress. However, the reliability of PPG as a stress biomarker in construction workers is hindered by motion artifacts (MA) - distortions in blood volume pulse measurements caused by sensor movement. This paper develops a deep convolutional autoencoder-based framework, trained to detect and reduce MA in MA-contaminated PPG signals. The framework's performance is evaluated using PPG signals acquired from individuals engaged in specific construction tasks. The results demonstrate the framework has effectiveness in both detecting and reducing MA in PPG signals with a detection accuracy of 93% and improvement in signal-to-noise ratio by over 88%. This research contributes to a more reliable and error-reduced usage of PPG signals for health monitoring in the construction industry. [Description provided by NIOSH]
• Subjects:
  Automation Biomarkers Construction Industry Fatigue Heat Stress Disorders Hot Temperature Mental Health Occupational Health Safety Stress, Psychological
• Keywords:
  Anomaly Detection Author Keywords: Construction Worker Health Monitoring Autoencoder Construction Industry Convolutional Neural Network Heat Stress Mental Stress Monitoring Systems Motion Artifact Neural Networks Photoplethysmography Sensors

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• ISSN:
  0926-5805
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Illinois ; OSHA Region 3 ; OSHA Region 5 ; Pennsylvania
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  165
• NIOSHTIC Number:
  nn:20069867
• Citation:
  Autom Constr 2024 Sep; 165:105537
• Contact Point Address:
  Houtan Jebelli, Department Civil and Environmental Engineering, University of Illinois Urbana-Campaign, Champaign, IL, United States of America
• Email:
  hjebelli@illinois.edu
• Federal Fiscal Year:
  2024
• 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:d55a4947a385ce3dc9864a44e3d03b9a1e8a2f05131fd06f5f0b7d23ce855e5aa5f7d6ceb15f8815877d374487fef75a8bea85de02e915b24a0d833fefd78b76
• Download URL:
  https://stacks.cdc.gov/view/cdc/208207/cdc_208207_DS1.pdf
• File Type:
  [PDF - 7.98 MB ]