Lightweight deep learning framework for recognizing construction workers' activities based on simplified node combinations

Yuanyuan Tian

doi:10.1016/j.autcon.2023.105236

Lightweight deep learning framework for recognizing construction workers' activities based on simplified node combinations

2024-02-01

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DOI：10.1016/j.autcon.2023.105236

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Abstract

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Monitoring construction workers' activities is vital to effective construction project management. However, most existing studies on skeleton-based worker activity recognition use full-body skeleton data, which involve inconvenient movement and high computational demands. This research aimed to identify simplified skeleton node combinations at various scales and develop a framework that reduces computational demands without sacrificing accuracy for the combinations. To this end, this study selected five node combinations at different scales using five deep learning algorithms and developed a lightweight deep learning framework by reducing input features and sample frequencies and stacking temporal convolution network (TCN) blocks. The results demonstrate that this framework outperforms the original deep learning algorithm utilizing the entire skeleton by approximately 1.94%-6.75%. This research contributes to the field of automated construction workers' ac-tivity recognition by reducing inconvenient movements and computational demands. Further research needs to investigate the relationships between sensor locations and specific types of motions.

Keywords:

Worker action recognition

Lightweight deep learning

Simplified skeleton node combinations

Reduced input feature dimension

Low sample frequency

Lightweight deep learning framework for recognizing construction workers' activities based on simplified node combinations

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