Transforming neurophysiological data into measurable stress metric using machine learning-based stress quantification framework for smart factory workers
- DOI
- 10.3389/fnrgo.2026.1871817
- Published
- 2026-10-21
- Container
- Frontiers in Neuroergonomics
- Publisher
- Frontiers Media SA
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3389/fnrgo.2026.1871817,
title = {Transforming neurophysiological data into measurable stress metric using machine learning-based stress quantification framework for smart factory workers},
author = {Arshia Arif and Zohreh Zakeri and Ahmet Omurtag and Philip Breedon and Azfar Khalid},
year = {2026},
journal = {Frontiers in Neuroergonomics},
doi = {10.3389/fnrgo.2026.1871817},
url = {https://doi.org/10.3389/fnrgo.2026.1871817}
}RIS
TY - JOUR TI - Transforming neurophysiological data into measurable stress metric using machine learning-based stress quantification framework for smart factory workers AU - Arshia Arif AU - Zohreh Zakeri AU - Ahmet Omurtag AU - Philip Breedon AU - Azfar Khalid PY - 2026 JO - Frontiers in Neuroergonomics DO - 10.3389/fnrgo.2026.1871817 UR - https://doi.org/10.3389/fnrgo.2026.1871817 ER -
APA
Arif, A., Zakeri, Z., Omurtag, A., Breedon, P., & Khalid, A. (2026). Transforming neurophysiological data into measurable stress metric using machine learning-based stress quantification framework for smart factory workers. Frontiers in Neuroergonomics. https://doi.org/10.3389/fnrgo.2026.1871817
Source records
- crossref · retrieved 2026-11-07T13:00:16.292Z