Improving Health Monitoring of Construction Workers Using Physiological Data-Driven Techniques: An Ensemble Learning-Based Framework to Address Distributional Shifts

Amit Ojha, Yizhi Liu, Houtan Jebelli, Hunayu Cheng, Mehdi Kiani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

While researchers have used various off-the-shelf physiological sensors and prevalent machine learning (ML) algorithms to objectively assess construction workers' health status, there remain specific challenges for consistent and accurate health monitoring on the jobsite. The existing physiological-based data-driven frameworks for predicting workers' health status in the field are not robust to the distribution shift of physiological signals and face challenges in stability, reliability, and accuracy. To overcome these issues, this paper proposes using an ensemble learning technique implemented on a support vector machine (SVM) with the Adaptive Boosting (AdaBoost) algorithm to develop a resilient predictive performance of the data-driven framework. To examine the performance of the framework, physiological signals were collected from 10 subjects performing material handling tasks with varying levels of physical fatigue. The proposed framework predicted the physical fatigue level with over 88% accuracy, better than single machine learning classifiers. This study has significant implications for improving the accuracy and stability of physiological-sensing-based health monitoring.

Original languageEnglish (US)
Title of host publicationComputing in Civil Engineering 2023
Subtitle of host publicationResilience, Safety, and Sustainability - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023
EditorsYelda Turkan, Joseph Louis, Fernanda Leite, Semiha Ergan
PublisherAmerican Society of Civil Engineers (ASCE)
Pages631-638
Number of pages8
ISBN (Electronic)9780784485248
DOIs
StatePublished - 2024
EventASCE International Conference on Computing in Civil Engineering 2023: Resilience, Safety, and Sustainability, i3CE 2023 - Corvallis, United States
Duration: Jun 25 2023Jun 28 2023

Publication series

NameComputing in Civil Engineering 2023: Resilience, Safety, and Sustainability - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023

Conference

ConferenceASCE International Conference on Computing in Civil Engineering 2023: Resilience, Safety, and Sustainability, i3CE 2023
Country/TerritoryUnited States
CityCorvallis
Period6/25/236/28/23

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Civil and Structural Engineering

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