Wearable Multi-Level Physical Fatigue Prediction in Manufacturing
Mohapatra P., Aravind V., Bisram M., et al.
Northwestern University / Boeing / Deere & Co. / University at Buffalo · 2024 · United States
Multimodal wearable data - a chest-mounted soft biosensor (ECG-derived heart rate, HRV, skin temperature) plus five torso/arm IMUs - with self-reported Borg ratings from 43 workers performing composite layup and wire-harnessing tasks with a weighted vest to induce fatigue, across five work segments plus rest, for multi-level fatigue prediction (PNAS Nexus, 2024).
Details
- Subjects
- n = 43
- Protocol / load
- Composite sheet layup and wire harnessing simulations with weighted vest
- Equipment
- Six wearable sensing locations: chest-mounted ANNE soft biosensor (ECG-derived heart rate, HRV, skin temperature) + five IMU nodes across torso and arms; Borg RPE scale
- Formats
csv- License
- CC-BY-4.0
- Keywords
- fatigue, manufacturing, wearables, borg-scale, physiological
- Added to catalog
- 2026-07-11
Cite
Cite the dataset as its authors request (check the source page for an official citation); this BibTeX is a convenience starting point.
@misc{nu-manufacturing-fatigue,
author = {{Mohapatra P., Aravind V., Bisram M., et al.}},
title = {{Wearable Multi-Level Physical Fatigue Prediction in Manufacturing}},
year = {2024},
doi = {10.5281/zenodo.12788571},
url = {https://doi.org/10.5281/zenodo.12788571},
}