Workers Fatigue Monitoring for Well-being Improvement in Manufacturing

Michel Rosselli; Vincenzo Cutrona; Samuele Dell'Oca; Elias Montini; Jože  Rožanec; Giusepe Landolfi; Christos Emmanouilidis; Andrea Bettoni

doi:10.1016/j.ifacol.2024.09.092

Workers Fatigue Monitoring for Well-being Improvement in Manufacturing

Michel Rosselli^*
, Vincenzo Cutrona
, Samuele Dell'Oca
, Elias Montini
, Jože Rožanec
, Giusepe Landolfi
, Christos Emmanouilidis
, Andrea Bettoni

^*Corresponding author for this work

Operations Management & Operations Research

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

4 Citations (Scopus)

129 Downloads (Pure)

Abstract

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.

Original language	English
Title of host publication	18th IFAC Symposium on Information Control Problems in Manufacturing (INCOM 2024)
Editors	Sebastian Schlund, Fazel Ansari
Publisher	Elsevier
Pages	61-66
Number of pages	6
DOIs	https://doi.org/10.1016/j.ifacol.2024.09.092
Publication status	Published - 2024
Event	18th IFAC Symposium on Information Control Problems in Manufacturing - Vienna, Vienna, Austria Duration: 28-Aug-2024 → 30-Aug-2024 https://www.incom2024.org/

Publication series

Name	IFAC PapersOnline
Publisher	Elsevier
Number	19
Volume	58
ISSN (Electronic)	2405-8963

Conference

Conference	18th IFAC Symposium on Information Control Problems in Manufacturing
Abbreviated title	INCOM 2024
Country/Territory	Austria
City	Vienna
Period	28/08/2024 → 30/08/2024
Internet address	https://www.incom2024.org/

Keywords

Industry 5.0
worker well-being
physical fatigue
wearable device
machine learning

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10.1016/j.ifacol.2024.09.092Licence: CC BY-NC-ND

Workers Fatigue Monitoring for Well-being Improvement in ManufacturingFinal publisher's version, 604 KBLicence: CC BY-NC-ND

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Rosselli, M., Cutrona, V., Dell'Oca, S., Montini, E., Rožanec, J., Landolfi, G., Emmanouilidis, C., & Bettoni, A. (2024). Workers Fatigue Monitoring for Well-being Improvement in Manufacturing. In S. Schlund, & F. Ansari (Eds.), 18th IFAC Symposium on Information Control Problems in Manufacturing (INCOM 2024) (pp. 61-66). (IFAC PapersOnline; Vol. 58, No. 19). Elsevier. https://doi.org/10.1016/j.ifacol.2024.09.092

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title = "Workers Fatigue Monitoring for Well-being Improvement in Manufacturing",

abstract = "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. ",

keywords = "Industry 5.0, worker well-being, physical fatigue, wearable device, machine learning",

author = "Michel Rosselli and Vincenzo Cutrona and Samuele Dell'Oca and Elias Montini and Jo{\v z}e Ro{\v z}anec and Giusepe Landolfi and Christos Emmanouilidis and Andrea Bettoni",

year = "2024",

doi = "10.1016/j.ifacol.2024.09.092",

language = "English",

series = "IFAC PapersOnline",

publisher = "Elsevier",

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pages = "61--66",

editor = "Sebastian Schlund and Fazel Ansari",

booktitle = "18th IFAC Symposium on Information Control Problems in Manufacturing (INCOM 2024)",

note = "18th IFAC Symposium on Information Control Problems in Manufacturing, INCOM 2024 ; Conference date: 28-08-2024 Through 30-08-2024",

url = "https://www.incom2024.org/",

}

Rosselli, M, Cutrona, V, Dell'Oca, S, Montini, E, Rožanec, J, Landolfi, G, Emmanouilidis, C & Bettoni, A 2024, Workers Fatigue Monitoring for Well-being Improvement in Manufacturing. in S Schlund & F Ansari (eds), 18th IFAC Symposium on Information Control Problems in Manufacturing (INCOM 2024). IFAC PapersOnline, no. 19, vol. 58, Elsevier, pp. 61-66, 18th IFAC Symposium on Information Control Problems in Manufacturing, Vienna, Austria, 28/08/2024. https://doi.org/10.1016/j.ifacol.2024.09.092

Workers Fatigue Monitoring for Well-being Improvement in Manufacturing. / Rosselli, Michel; Cutrona, Vincenzo; Dell'Oca, Samuele et al.
18th IFAC Symposium on Information Control Problems in Manufacturing (INCOM 2024). ed. / Sebastian Schlund; Fazel Ansari. Elsevier, 2024. p. 61-66 (IFAC PapersOnline; Vol. 58, No. 19).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Workers Fatigue Monitoring for Well-being Improvement in Manufacturing

AU - Rosselli, Michel

AU - Cutrona, Vincenzo

AU - Dell'Oca, Samuele

AU - Montini, Elias

AU - Rožanec, Jože

AU - Landolfi, Giusepe

AU - Emmanouilidis, Christos

AU - Bettoni, Andrea

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

KW - Industry 5.0

KW - worker well-being

KW - physical fatigue

KW - wearable device

KW - machine learning

U2 - 10.1016/j.ifacol.2024.09.092

DO - 10.1016/j.ifacol.2024.09.092

M3 - Conference contribution

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SP - 61

EP - 66

BT - 18th IFAC Symposium on Information Control Problems in Manufacturing (INCOM 2024)

A2 - Schlund, Sebastian

A2 - Ansari, Fazel

PB - Elsevier

T2 - 18th IFAC Symposium on Information Control Problems in Manufacturing

Y2 - 28 August 2024 through 30 August 2024

ER -

Workers Fatigue Monitoring for Well-being Improvement in Manufacturing

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