Real-Time Physiological Fatigue Prediction for Human–Robot Collaborative Manufacturing Using Wearable Sensor Fusion and Hybrid Deep Learning: An In Silico Digital Twin Study
- DOI
- 10.3390/s26175556
- Published
- 2026-09-01
- Container
- Sensors
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/s26175556,
title = {Real-Time Physiological Fatigue Prediction for Human–Robot Collaborative Manufacturing Using Wearable Sensor Fusion and Hybrid Deep Learning: An In Silico Digital Twin Study},
author = {Claudio Urrea},
year = {2026},
journal = {Sensors},
doi = {10.3390/s26175556},
url = {https://doi.org/10.3390/s26175556}
}RIS
TY - JOUR TI - Real-Time Physiological Fatigue Prediction for Human–Robot Collaborative Manufacturing Using Wearable Sensor Fusion and Hybrid Deep Learning: An In Silico Digital Twin Study AU - Claudio Urrea PY - 2026 JO - Sensors DO - 10.3390/s26175556 UR - https://doi.org/10.3390/s26175556 ER -
APA
Urrea, C. (2026). Real-Time Physiological Fatigue Prediction for Human–Robot Collaborative Manufacturing Using Wearable Sensor Fusion and Hybrid Deep Learning: An In Silico Digital Twin Study. Sensors. https://doi.org/10.3390/s26175556
Source records
- crossref · retrieved 2026-09-24T19:27:48.853Z