Exploring the applicability of physiological monitoring to manage physical fatigue in firefighters

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Denisse Bustos (Author) Other; Ricardo Cardoso (Author) Other; Diogo D. Carvalho (Author) Other; Joana Guedes (Author) FEUP; Mário Vaz (Author) FEUP; José Castela Costa (Author) FMUP; João Santos Baptista (Author) Other; Ricardo J. Fernandes (Author) FADEUP

Journal

Title: Sensors

Vol. 23 No. 11

Pages: 5127/1-5127/13

ISSN: 1424-3210

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

Scopus

Scientific classification

FOS: Social sciences

Other information

Authenticus ID: P-00Y-FET

DOI: 10.3390/s23115127

Abstract (EN): Physical fatigue reduces productivity and quality of work while increasing the risk of injuries and accidents among safety-sensitive professionals. To prevent its adverse effects, researchers are developing automated assessment methods that, despite being highly accurate, require a comprehensive understanding of underlying mechanisms and variables' contributions to determine their real-life applicability. This work aims to evaluate the performance variations of a previously developed four-level physical fatigue model when alternating its inputs to have a comprehensive view of the impact of each physiological variable on the model's functioning. Data from heart rate, breathing rate, core temperature and personal characteristics from 24 firefighters during an incremental running protocol were used to develop the physical fatigue model based on an XGBoosted tree classifier. The model was trained 11 times with different input combinations resulting from alternating four groups of features. Performance measures from each case showed that heart rate is the most relevant signal for estimating physical fatigue. Breathing rate and core temperature enhanced the model when combined with heart rate but showed poor performance individually. Overall, this study highlights the advantage of using more than one physiological measure for improving physical fatigue modelling. The findings can contribute to variables and sensor selection in occupational applications and as the foundation for further field research.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13