Publications

Detailed Information

Human-independent activity recognition of construction worker

Cited 0 time in Web of Science Cited 0 time in Scopus
Authors

Park, Seongeun; Lee, Hoonyong; Ahn, Changbum Ryan; Park, Moonseo

Issue Date
2023
Publisher
European Council on Computing in Construction (EC3)
Citation
Proceedings of the European Conference on Computing in Construction
Abstract
With recent advancements in sensor and data analysis technology, multiple research on worker activity recognition through wearable sensors have been conducted to solve worker safety and productivity problem at construction sites. However, most rely on pre-trained models which require re-training of each worker to take into account differences between workers. To alleviate this limitation, we propose a human-independent model that can adapt to differences in workers. Our model uses variational-denoising autoencoder with soft parameter sharing to extract common features in different construction activities, achieving 78.64% accuracy which is higher than existing benchmark models.
ISSN
2684-1150
URI
https://hdl.handle.net/10371/203347
DOI
https://doi.org/10.35490/EC3.2023.317
Files in This Item:
There are no files associated with this item.
Appears in Collections:

Related Researcher

  • College of Engineering
  • Department of Architecture & Architectural Engineering
Research Area Computing in Construction, Management in Construction

Altmetrics

Item View & Download Count

  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Share