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Sensor-Based Automatic Recognition of Construction Worker Activities Using Deep Learning Network.

Ömür Tezcan1, Cemil Akcay2, Mahmut Sari3

  • 1Institute of Science, Istanbul University-Cerrahpaşa, 34320 Istanbul, Türkiye.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
Summary

This study introduces deep learning (DL) for construction worker activity recognition using sensor data. The developed model achieved high accuracy, showing potential for improved workforce management and site productivity.

Keywords:
BiLSTMLSTMconstruction automationdeep learninghuman activity recognitionmotion sensorsproductivity analysiswearable sensors

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Area of Science:

  • Engineering
  • Computer Science
  • Construction Management

Background:

  • Construction industry relies heavily on manual labor, unlike other sectors adopting automation.
  • Need for enhanced operational efficiency and productivity in labor-intensive construction environments.

Purpose of the Study:

  • To develop a decision-support framework for automated human activity recognition in construction.
  • To mitigate productivity losses and improve time and cost efficiency through DL.

Main Methods:

  • Collected sensor data (acceleration and position) from five construction workers across eleven body locations.
  • Conducted recognition experiments using acceleration data, position data, and a combined dataset.
  • Utilized a deep learning architecture with long short-term memory (LSTM) and bidirectional long-term memory (BiLSTM) layers.

Main Results:

  • Achieved high classification accuracy using the proposed DL architecture.
  • Accuracy rates of 98.1% for acceleration-only data and 99.6% for combined acceleration and position data.
  • Demonstrated the effectiveness of DL for real-time human activity recognition in construction.

Conclusions:

  • Deep learning approaches are highly effective for real-time human activity recognition in construction.
  • Automated activity detection can significantly improve workforce management and site productivity.
  • The proposed framework offers a viable solution for enhancing efficiency in the construction sector.