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

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.

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

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.

Safety and health risks for workers exposed to cold thermal environments: A frozen food processing industry perspective.

BACKGROUND: Environmental temperatures in the fresh food industry vary from 0°C to 10°C, and go below -20°C for the frozen food industry, representing risk for the health and safety of workers involved. OBJECTIVE: The aim of this work was to evaluate the cold thermal stress risks for workers working in a frozen food industry. METHODS: A total of 27 acclimatized workers (13 male and 14 female) participated in a study which was conducted during 11 working days. The thermal sensation questionnaire and the cold work health questionnaire (CWHQ) were applied to all participants. Additionally, 4 workers were chosen to be fully monitored with a thermometer telemetry capsule for measuring the intra-abdominal temperature and 8 skin temperature sensors. RESULTS: The lowest recorded hand temperature was 14.09°C, lowest forehead 18.55°C, mean skin temperature had variations of 1.10 to 3.20°C along the working period. Highest and most frequent fluctuations were found in the hand and forehead skin temperatures, small changes were found in mean skin temperature. CONCLUSIONS: Answers to the CWHQ increase concern on clinical forms of "a frigore", and in two cases the mean body temperature decreased below 35.0°C, which is defined in the current literature as a mild form of hypothermia.

Influence of severe cold thermal environment on core and skin temperatures: A systematic review.

BACKGROUND: Exposure to severe cold thermal environment (SCE) is a significant risk factor in the frozen food industry, influencing health and safety of the employees. OBJECTIVE: The aim of this work is to present the level of knowledge on the influence of SCE on core and skin temperatures. METHODS: The review has been conducted using appropriated keywords and expressions, by searching 21 electronic databases and references of the included articles. Only research articles with healthy subjects and considering exposure to SCE conditions (- 5°C or lower) were considered. RESULTS: Thirteen articles were included in the systematic review which met the research objective and were in accordance with the inclusion and exclusion criteria. All the included studies measured core or skin temperatures. CONCLUSIONS: The main findings of this review indicate that working in SCE is and will remain an added risk factor. Further studies should be conducted in laboratory and industrial severe cold thermal environments on acclimatized and non-acclimatized subjects, in order to evaluate core and skin temperature variations and its recovery periods.