Workers Fatigue Monitoring for Well-being Improvement in Manufacturing

In Industry 5.0, worker well-being is paramount for organizational resilience and sustainability. Physical fatigue, work-life balance, and job competency significantly impact worker welfare and, therefore, efficiency and effectiveness. This study collects data in different industrial scenarios using...

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Bibliographic Details
Published in:IFAC-PapersOnLine 2024, Vol.58 (19), p.61-66
Main Authors: Rosselli, Michel, Cutrona, Vincenzo, Dell’Oca, Samuele, Montini, Elias, Rožanec, Jože M., Landolfi, Giuseppe, Emmanouilidis, Christos, Bettoni, Andrea
Format: Article
Language:English
Subjects:
Industry 5.0
machine learning
physical fatigue
wearable devices
worker well-being
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Summary:In Industry 5.0, worker well-being is paramount for organizational resilience and sustainability. Physical fatigue, work-life balance, and job competency significantly impact worker welfare and, therefore, efficiency and effectiveness. This study collects data in different industrial scenarios using non-invasive wearable devices for dynamic data and questionnaires for quasi-static data. Using Machine Learning algorithms, including Random Forest and Feedforward Neural Network models, the study predicts the physical fatigue of workers across multi-class and binary classifications. The developed Fatigue Monitoring System software integrates these models to monitor fatigue and improve worker well-being.
ISSN:2405-8963
2405-8963
DOI:10.1016/j.ifacol.2024.09.092