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Construction motion data library: an integrated motion dataset for on-site activity recognition.

Yuanyuan Tian1, Heng Li2, Hongzhi Cui3

  • 1Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong SAR, China.

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This study created a large construction motion data library (CML) for activity recognition. The dataset enables accurate identification of construction worker activities, improving site safety and productivity.

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Area of Science:

  • Computer Science
  • Robotics
  • Human-Computer Interaction

Background:

  • Automated worker activity recognition is vital for construction site safety and productivity.
  • Existing motion datasets lack specificity for construction's unique demands, like awkward postures and intensive tasks.
  • Vision-based and inertial sensors are commonly used for 3D human skeleton construction and activity recognition.

Purpose of the Study:

  • To develop a specialized dataset and manually label a large-scale construction motion data library (CML) for activity recognition.
  • To address the limitations of generic motion datasets for construction-specific activities.
  • To evaluate the performance of deep learning models on the newly created CML dataset.

Main Methods:

  • An in-lab experiment was conducted to create a small, construction-related activity dataset.
  • This dataset was used to manually label a large-scale construction motion data library (CML).
  • Five widely applied deep learning algorithms were employed to examine the CML dataset's usability, quality, and sufficiency.

Main Results:

  • The CML dataset comprises 225 activity types and 146,480 samples, with 60 types (61,275 samples) specifically related to construction.
  • Deep learning models, without tuning, achieved an average accuracy ranging from 74.62% to 83.92% on the dataset.
  • The results demonstrate the dataset's quality and sufficiency for training activity recognition models.

Conclusions:

  • The developed CML dataset is a valuable resource for construction activity recognition research.
  • The dataset enables more accurate and reliable identification of construction worker activities.
  • This advancement can significantly contribute to enhanced safety and productivity in the construction industry.