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Research on Construction Workers' Activity Recognition Based on Smartphone.

Mingyuan Zhang1, Shuo Chen2, Xuefeng Zhao3

  • 1Department of Construction Management, Dalian University of Technology, Dalian 116000, China. myzhang@dlut.edu.cn.

Sensors (Basel, Switzerland)
|August 17, 2018
PubMed
Summary
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This study uses smartphone sensors to classify construction worker activities, achieving high accuracy. This demonstrates the feasibility of using mobile devices for construction site monitoring and management.

Area of Science:

  • Construction Management
  • Human Activity Recognition
  • Wearable Technology

Background:

  • Traditional sensor systems lack flexibility and stability for complex construction environments.
  • Monitoring individual construction worker activities is crucial for site management.
  • Existing methods struggle to meet the dynamic demands of construction sites.

Purpose of the Study:

  • To develop and validate a smartphone-based system for construction worker activity recognition.
  • To assess the effectiveness of using embedded smartphone sensors (accelerometer, gyroscope) for activity classification.
  • To improve the accuracy and feasibility of construction activity monitoring.

Main Methods:

  • Utilized smartphone accelerometers and gyroscopes to collect data on eight common construction activities.
Keywords:
activity recognitionconstruction managementfeature extractionmachine learningsensorsmartphone

Related Experiment Videos

  • Acquired data from multiple body parts to enhance feature dimensionality for better activity discrimination.
  • Employed the Classification and Regression Trees (CART) algorithm for classification model training and validation.

    • Main Results:

    • Achieved an overall classification accuracy of 89.85% for construction worker activities.
    • Reached a prediction accuracy of 94.91% using the developed model.
    • Demonstrated the effectiveness of combining smartphone data with decision tree algorithms.

    Conclusions:

    • Smartphones are feasible and effective tools for data acquisition in construction management.
    • The proposed approach using smartphones and decision trees enables accurate classification of complex worker activities.
    • This technology offers a flexible and stable solution for monitoring individuals in construction environments.