Institute of Electronics and Informatics Engineering of Aveiro (IEETA) & Department of Electronics, Telecommunications and Informatics (DETI), University of Aveiro, 3810-193 Aveiro, Portugal.
Centre for Environmental and Marine Studies (CESAM) & Department of Environment and Planning (DAO), University of Aveiro, 3810-193 Aveiro, Portugal.
Sensors (Basel). 2021 Feb 24;21(5):1561. doi: 10.3390/s21051561.
Smoke inhalation poses a serious health threat to firefighters (FFs), with potential effects including respiratory and cardiac disorders. In this work, environmental and physiological data were collected from FFs, during experimental fires performed in 2015 and 2019. Extending a previous work, which allowed us to conclude that changes in heart rate (HR) were associated with alterations in the inhalation of carbon monoxide (CO), we performed a HR analysis according to different levels of CO exposure during firefighting based on data collected from three FFs. Based on HR collected and on CO occupational exposure standards (OES), we propose a classifier to identify CO exposure levels through the HR measured values. An ensemble of 100 bagged classification trees was used and the classification of CO levels obtained an overall accuracy of 91.9%. The classification can be performed in real-time and can be embedded in a decision fire-fighting support system. This classification of FF' exposure to critical CO levels, through minimally-invasive monitored HR, opens the possibility to identify hazardous situations, preventing and avoiding possible severe problems in FF' health due to inhaled pollutants. The obtained results also show the importance of future studies on the relevance and influence of the exposure and inhalation of pollutants on the FF' health, especially in what refers to hazardous levels of toxic air pollutants.
烟雾吸入对消防员(FFs)构成严重的健康威胁,潜在影响包括呼吸道和心脏疾病。在这项工作中,从 2015 年和 2019 年进行的实验火灾中收集了 FFs 的环境和生理数据。在之前的工作中,我们得出结论,心率(HR)的变化与一氧化碳(CO)吸入的变化有关,我们根据从三名 FFs 收集的数据,根据消防过程中 CO 暴露的不同水平,对 HR 进行了分析。根据收集到的 HR 和 CO 职业暴露标准(OES),我们提出了一种通过测量 HR 值来识别 CO 暴露水平的分类器。使用了 100 个袋装分类树的集成,CO 水平的分类总体准确率为 91.9%。分类可以实时进行,并可以嵌入决策性消防支持系统中。通过最小侵入性监测 HR 对 FFs 暴露于临界 CO 水平进行分类,为识别危险情况提供了可能性,防止和避免由于吸入污染物而导致 FFs 健康可能出现的严重问题。所获得的结果还表明,未来需要对污染物的暴露和吸入对 FFs 健康的相关性和影响进行研究,特别是在涉及有毒空气污染物的危险水平方面。