Physiological Monitoring Systems for Fatigue Detection Within Firefighters: A Brief Systematic Review

Pratas, P.; Bustos, D.; Guedes, Joana; Mendes, Joaquim; Baptista, J. Santos; Vaz, Mário

doi:10.1007/978-3-031-12547-8_38

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…The first three variables were selected as the main variables and therefore included in the model, since they have been previously proven as valid and reliable indicators of physical exertion and fatigue within various occupational settings [ 13 , 16 ]. Furthermore, they can be obtained using sensors that allow mobility, continuous monitoring and ease of wearing [ 13 , 14 , 17 , 33 ]. For the main and complementary measured variables, the normality of distribution was checked using the Shapiro–Wilk’s test and mean values ± SD were calculated for every stage.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Approach to Model Physical Fatigue during Incremental Exercise among Firefighters

Bustos

Cardoso

Rios

et al. 2022

Sensors

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Physical fatigue is a serious threat to the health and safety of firefighters. Its effects include decreased cognitive abilities and a heightened risk of accidents. Subjective scales and, recently, on-body sensors have been used to monitor physical fatigue among firefighters and safety-sensitive professionals. Considering the capabilities (e.g., noninvasiveness and continuous monitoring) and limitations (e.g., assessed fatiguing tasks and models validation procedures) of current approaches, this study aimed to develop a physical fatigue prediction model combining cardiorespiratory and thermoregulatory measures and machine learning algorithms within a firefighters’ sample. Sensory data from heart rate, breathing rate and core temperature were recorded from 24 participants during an incremental running protocol. Various supervised machine learning algorithms were examined using 21 features extracted from the physiological variables and participants’ characteristics to estimate four physical fatigue conditions: low, moderate, heavy and severe. Results showed that the XGBoosted Trees algorithm achieved the best outcomes with an average accuracy of 82% and accuracies of 93% and 86% for recognising the low and severe levels. Furthermore, this study evaluated different methods to assess the models’ performance, concluding that the group cross-validation method presents the most practical results. Overall, this study highlights the advantages of using multiple physiological measures for enhancing physical fatigue modelling. It proposes a promising health and safety management tool and lays the foundation for future studies in field conditions.

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Section: Methodsmentioning

confidence: 99%

Machine Learning Approach to Model Physical Fatigue during Incremental Exercise among Firefighters

Bustos

Cardoso

Rios

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…Signals from heart rate, breathing rate, and core temperature were used as the main variables for modelling since they can be obtained using sensors feasible to be used within extreme environments, allowing mobility, uninterrupted monitoring, and comfort [32][33][34]. Heart rate is the most easily measured vital sign, being widely used to measure physical exertion and overload [18] and to determine the physiological demands and workload of firefighting activities [35,36].…”

Section: Physiological Variablesmentioning

confidence: 99%

Exploring the Applicability of Physiological Monitoring to Manage Physical Fatigue in Firefighters

Bustos

Cardoso

Carvalho

et al. 2023

Sensors

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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.

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Bioseñales en entornos laborales y su aplicación en primeros intervinientes

Vera Ortega,

Vázquez Martín,

Mandow

et al. 2024

JA-CEA

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La medición de señales psicofisiológicas de trabajadores en el desempeño de sus tareas es útil para detectar estados psicofisiológicos que les impidan desarrollar adecuadamente su labor y pongan en peligro su integridad física. Para una detección efectiva de estos estados es necesario una selección adecuada de las bioseñales a monitorizar, acorde a la labor realizada, y un procesamiento correcto de las mismas. También es necesario establecer una verdad fundamental que permita el desarrollo de algoritmos de aprendizaje automático efectivas. Este artículo revisa las bioseñales y herramientas de procesamiento y predicción utilizadas en la detección de estados psicofisiológicos peligrosos para los trabajadores y expone una aplicación de monitorización de las bioseñales con primeros intervinientes durante ejercicios de alta fidelidad.

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Physiological Monitoring Systems for Fatigue Detection Within Firefighters: A Brief Systematic Review

Cited by 3 publications

References 35 publications

Machine Learning Approach to Model Physical Fatigue during Incremental Exercise among Firefighters

Machine Learning Approach to Model Physical Fatigue during Incremental Exercise among Firefighters

Exploring the Applicability of Physiological Monitoring to Manage Physical Fatigue in Firefighters

Bioseñales en entornos laborales y su aplicación en primeros intervinientes

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