Spatiotemporal graph convolutional network for robust three-dimensional worker pose estimation from single-camera video with application to real-time ergonomic risk assessment.
- DOI
- 10.1186/s42492-026-00237-x
- Published
- 2026 Nov 9
- Container
- Visual computing for industry, biomedicine, and art
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1186/s42492-026-00237-x,
title = {Spatiotemporal graph convolutional network for robust three-dimensional worker pose estimation from single-camera video with application to real-time ergonomic risk assessment.},
author = {Bonyani M and Soleymani M and Wang C and Jiang N},
year = {2026},
journal = {Visual computing for industry, biomedicine, and art},
doi = {10.1186/s42492-026-00237-x},
url = {https://doi.org/10.1186/s42492-026-00237-x}
}RIS
TY - JOUR TI - Spatiotemporal graph convolutional network for robust three-dimensional worker pose estimation from single-camera video with application to real-time ergonomic risk assessment. AU - Bonyani M AU - Soleymani M AU - Wang C AU - Jiang N PY - 2026 JO - Visual computing for industry, biomedicine, and art DO - 10.1186/s42492-026-00237-x UR - https://doi.org/10.1186/s42492-026-00237-x ER -
APA
M, B., M, S., C, W., & N, J. (2026). Spatiotemporal graph convolutional network for robust three-dimensional worker pose estimation from single-camera video with application to real-time ergonomic risk assessment.. Visual computing for industry, biomedicine, and art. https://doi.org/10.1186/s42492-026-00237-x
Source records
- pubmed · retrieved 2026-11-10T04:44:03.280Z