Construction Activity Analysis of Workers Based on Human Posture Estimation Information

Xuhong Zhou, Shuai Li, Jiepeng Liu, Zhou Wu, Yohchia Frank Chen

Research output: Contribution to journalArticlepeer-review


Identifying workers’ construction activities or behaviors can enable managers to better monitor labor efficiency and construction progress. However, current activity analysis methods for construction workers rely solely on manual observations and recordings, which consumes considerable time and has high labor costs. Researchers have focused on monitoring on-site construction activities of workers. However, when multiple workers are working together, current research cannot accurately and automatically identify the construction activity. This research proposes a deep learning framework for the automated analysis of the construction activities of multiple workers. In this framework, multiple deep neural network models are designed and used to complete worker key point extraction, worker tracking, and worker construction activity analysis. The designed framework was tested at an actual construction site, and activity recognition for multiple workers was performed, indicating the feasibility of the framework for the automated monitoring of work efficiency.

Original languageEnglish (US)
StateAccepted/In press - 2024

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Environmental Engineering
  • General Chemical Engineering
  • Materials Science (miscellaneous)
  • Energy Engineering and Power Technology
  • General Engineering

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