Abstract
Firefighters can suffer serious health problems and experience cardiac disorders derived from high pollutants inhalation. During experimental field burns, environmental and heart rate data from firefighters were collected and it was possible to observe that changes in heart rate were related with variations in pollutants inhalation. Therefore, detecting changes in heart rate may provide a good indicator to identify hazardous situations for firefighters. An automated method, based on the detection of changes in the heart rate, is proposed to prevent and to avoid serious undesirable side-effects in the health of firefighters due to pollutants inhalation. Within the experiments performed, a precision and a recall of 91.5 and 78.2%, respectively, were obtained. Furthermore, this approach can be part of a real-time decision support system for routine use in firefighting practice. Our results show the potential to provide effective support in real operational scenarios and that further research on the impact of environmental conditions in the well-being of firefighters is of utmost importance.






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According to the American Conference of Governmental Industrial Hygienists.
References
(2017) The consequences of fire. http://www.nfpa.org/news-and-research/news-and-media/press-room/reporters-guide-to-fire-and-nfpa/consequences-of-fire. Accessed 20 Apr 2017
Aguiar A, Soares E, Brandão P, Magalhães T, Fernandes JM, Oliveira I (2014) Demo: wireless ip mesh on android for fire-fighters monitoring. In: Proceedings of the 9th ACM MobiCom workshop on challenged networks, CHANTS ’14, New York, NY, USA, pp 89–92
Amorim J, Valente J, Cascão P, Ribeiro L, Viegas D, Ottmar R, Miranda A (2016) Near-source grid-based measurement of co and pm2.5 concentration during a full-scale fire experiment in southern European Shrubland. Atmos Environ 145:19–28
Baena-García M, Campo-Ávila JD, Fidalgo R, Bifet A, Gavaldá R, Morales-Bueno R (2006) Early drift detection method. In: 4th ECML PKDD international workshop on knowledge discovery from data streams, pp 77–86
Basseville M, Nikiforov I (1993) Detection of abrupt changes: theory and applications. Prentice-Hall, Englewood Cliffs, NJ, USA
Bifet A, Gavaldá R (2007) Learning from time-changing data with adaptive windowing. In: SIAM international conference on data mining, Berlin, Heidelberg
Borges MRS, Ochoa SF, Pino JA, Vivacqua AS (2010) Assigning emergency vehicles to urban incidents. In: Respício A, Adam F, Phillips-Wren GE, Teixeira C, Telhada J (eds) Bridging the socio-technical gap in decision support systems. IOS Press. ISBN: 978 1 60750 576 1
Calvão AR, Carvalho F, Marques F (2015) Decision support system for forest fires firefighting in agueda municipality. In: 2015 10th Iberian conference on information systems and technologies (CISTI), pp 1–5
Cunha JPS, Cunha B, Pereira AS, Xavier W, Ferreira N, Meireles L (2010) Vital-jacket®: a wearable wireless vital signs monitor for patients’ mobility in cardiology and sports. In: 4th international ICST conference on pervasive computing technologies for healthcare (PervasiveHealth), pp 1–2
Dasu T, Krishnan S, Venkatasubramanian S, Yi K (2006) An information-theoretic approach to detecting changes in multi-dimensional data streams. In: Proc. Symp. on the interface of statistics, computing science, and applications
Finlay SE, Moffat A, Gazzard R, Baker D, Murray V (2012) Health impacts of wildfires. PLoS Curr 4:e4f959951cce2c
Gama J (2010) Knowledge discovery from data streams, 1st edn. Chapman & Hall/CRC. ISBN: 978 1 43982 611 9
Gama J, Medas P, Castillo G, Rodrigues P (2004) Learning with drift detection. In: SBIA Brazilian symposium on artificial intelligence. Springer Verlag, pp 286–295
Gama J, Sebastião R, Rodrigues PP (2013) On evaluating stream learning algorithms. Mach Learn 90(3):317–346
Hartland C, Gelly S, Baskiotis N, Teytaud O, Sebag M (2006) Multi-armed bandit, dynamic environments and meta-bandits. https://hal.archives-ouvertes.fr/hal-00113668/file/MetaEve.pdf
Henderson S, Johnston F (2012) Measures of forest fire smoke exposure and their associations with respiratory health outcomes. Curr Opin Allergy Clin Immunol 12:221–227
Hinkley DV (1971) Inference about the change-point from cumulative sum tests. Biometrika 58(3):509–523
Kifer D, Ben-David S, Gehrke J (2004) Detecting change in data streams. In: Proceedings of the thirtieth international conference on very large data bases, vol 30, VLDB ’04, pp 180–191. (VLDB Endowment)
Lazaridis M, Latos M, Aleksandropoulou V, Hov Ø, Papayannis A, Tørseth K (2008) Contribution of forest fire emissions to atmospheric pollution in Greece. Air Qual Atmos Health 1(3):143–158
Liu D, Tager I, Balmes J, Harrison R (1992) The effect of smoke inhalation on lung function and airway responsiveness in wildland fire fighters. Am Rev Respir Dis 146(6):1469–1473
Magalhães T, Oliveira I, Fernandes J (2015) Message based integration in cyber-physical system: firefighters in the field. In: Proceedings of the 12th EAI international conference on mobile and ubiquitous systems: computing, networking and services, MOBIQUITOUS’15, Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (ICST), Brussells, Belgium, pp 285–286
Martins V, Miranda A, Carvalho A, Schaap M, Borrego C, Sá E (2012) Impact of forest fires on particulate matter and ozone levels during the 2003, 2004 and 2005 fire seasons in Portugal. Sci Total Environ 414:53–62
Miranda A, Coutinho M, Borrego C (1994) Forest fire emissions in Portugal: a contribution to global warming? Environ Pollut 83(1):121–123
Miranda A, Ferreira J, Valente J, Santos P, Amorim J, Borrego C (2005) Smoke measurements during gestosa 2002 experimental fires. Int J Wildland Fire 14(1):107–116
Miranda AI, Monteiro A, Martins V, Carvalho A, Schaap M, Builtjes P, Borrego C (2008) Forest fires impact on air quality over Portugal. Springer, Netherlands, Dordrecht, pp 190–198
Miranda AI, Martins V, Cascão P, Amorim JH, Valente J, Tavares R, Borrego C, Tchepel O, Ferreira AJ, Cordeiro CR, Viegas DX, Ribeiro LM, Pita LP (2010) Monitoring of firefighters exposure to smoke during fire experiments in Portugal. Environ Int 36(7):736–745
Miranda A, Martins V, Cascão P, Amorim J, Valente J, Borrego C, Ferreira A, Cordeiro C, Viegas D, Ottmar R (2012) Wildland smoke exposures values and exhaled breath indicator in firefighters. J Toxicol Environ Health 75:831–843
Monares A, Ochoa SF, Pino JA, Herskovic V, Rodriguez-Covili J, Neyem A (2011) Mobile computing in urban emergency situations: improving the support to firefighters in the field. Expert Syst Appl 38(2):1255–1267
Monteiro A, Corti P, Miguel-Ayanz JS, Miranda A, Borrego C (2014) The Effis forest fire atmospheric emission model: application to a major fire event in Portugal. Atmos Environ 84:355–362
Mouss H, Mouss D, Mouss N, Sefouhi L (2004) Test of Page–Hinckley, an approach for fault detection in an agro-alimentary production system. In: Control conference, 2004. 5th Asian, vol 2, pp 815 –818
Page ES (1954) Continuous inspection schemes. Biometrika 41(1–2):100–115
Poor HV, Hadjiliadis O (2009) Quickest detection. Cambridge University Press, New York, USA
Ross GJ, Adams NM, Tasoulis DK, Hand DJ (2012) Exponentially weighted moving average charts for detecting concept drift. Pattern Recognit Lett 33(2):191–198
Rothman N, Ford D, Baser M, Hansen J, O’toole T, Tockman M, Strickland PT (1991) Pulmonary function and respiratory symptoms in wildland firefighters. J Occup Med 11(33):1163–1169
Schweizer DW, Cisneros R (2017) Forest fire policy: change conventional thinking of smoke management to prioritize long-term air quality and public health. Air Qual Atmos Health 10(1):33–36
Scotto MG, Gouveia S, Carvalho A, Monteiro A, Martins V, Flannigan MD, San-Miguel-Ayanz J, Miranda AI, Borrego C (2014) Area burned in Portugal over recent decades: an extreme value analysis. Int J Wildland Fire 23:812–824
Sebastião R, Gama J (2007) Change detection in learning histograms from data streams. In: Proceedings of the 13th Portuguese conference on artificial intelligence, EPIA’07. Springer-Verlag, Berlin, Heidelberg, pp 112–123
Sebastião R, Sorte S, Valente J, Miranda AI, Fernandes JM (2016) Inhalation during fire experiments: an approach derived through ecg. In: Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing: adjunct, UbiComp ’16. New York, NY, USA, pp 763–772
Sebastião R, Gama J, Mendonça T (2017) Fading histograms in detecting distribution and concept changes. Int J Data Sci Anal 3(3):183–212
World Health Organization (2013) Health effects of particulate matter. Policy implications for countries in eastern Europe, Caucasus and central Asia. World Health Organization. Regional Office for Europe. ISBN: 978 92 890 0001 7
Acknowledgements
A particular acknowledge should be given to Domingos Xavier Viegas and his research team for the organization and performing of the Gestosa experiments. This work was supported by the Portuguese Science Foundation (FCT) through national funds, and co-funded by FEDER, within the PT2020 Partnership Agreement and Compete 2020, under the projects IEETA (UID/CEC/00127/2013), VitalResponder2 (PTDC/EEI-ELC/2760/2012), VR2market (CMUP-ERI/FIA/0031/2013) and SOCA (CENTRO-01-0145-FEDER-000010). The Post-Doc grants of R. Sebastião (BPD/UI62/6777/2015 and BPD/UI62/6777/2018) and the PhD grant of S. Sorte (SFRH/BD/117164/2016) are also acknowledged.
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Sebastião, R., Sorte, S., Valente, J. et al. Detecting changes in the heart rate of firefighters to prevent smoke inhalation and health effects. Evolving Systems 10, 295–304 (2019). https://doi.org/10.1007/s12530-018-9241-0
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DOI: https://doi.org/10.1007/s12530-018-9241-0