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Risk Prevention of Spreading Emerging Infectious Diseases Using a HybridCrowdsensing Paradigm, Optical Sensors, and Smartphone

  • Mobile & Wireless Health
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

The risk of spreading diseases within (ad-hoc)crowds and the need to pervasively screen asymptomatic individuals to protect the population against emerging infectious diseases, request permanentcrowd surveillance., particularly in high-risk regions. Thecase of Ebola epidemic in West Africa in recent years has shown the need for pervasive screening. The trend today in diseases surveillance is consisting of epidemiological data collection about emerging infectious diseases using social media, wearable sensors systems, or mobile applications and data analysis. This approach presents various limitations. This paper proposes a novel approach for diseases monitoring and risk prevention of spreading infectious diseases. The proposed approach, aiming at overcoming the limitation of existing disease surveillance approaches, combines the hybrid crowdsensing paradigm with sensing individuals’ bio-signals using optical sensors for monitoring any risks of spreading emerging infectious diseases in any (ad-hoc) crowds. A proof-of-concept has been performed using a drone armed with a cat s60 smartphone featuring a Forward Looking Infra-Red (FLIR) camera. According to the results of the conducted experiment, the concept has the potential to improve the conventional epidemiological data collection. The measurement is reliable, and the recorded data are valid. The measurement error rates are about 8%.

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Notes

  1. www.foodbornechicago.org

  2. https://flunearyou.org

  3. http://www.fraden.com/Resources/Fraden_PressRel-2014.pdf

  4. VESNA is a modular and fully flexible platform for the development of wireless sensor networks developed at the SensorLab @ Jozef Stefan Institute. (http://sensorlab.ijs.si/hardware.html)

References

  1. Christaki, E., New technologies in predicting, preventing and controlling emerging infectious diseases. Virulence 6(6):558–565, 2015.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Brownstein, J. S., Freifeld, C. C., Reis, B. Y., and Mandl, K. D., Surveillance sans frontières: Internet-based emerging infectious disease intelligence and the HealthMap project. PLoS Med. 5(7):1019–1024, 2008.

    Article  Google Scholar 

  3. Edoh, T. O., and Teege, G., Using information technology for an improved pharmaceutical care delivery in developing countries. Study case: Benin. J. Med. Syst. 35(5):1123–1134, 2011.

    Article  PubMed  Google Scholar 

  4. Biswas, P., Duarte, C., Langdon, P., and Almeida, L. (Eds), A multimodal end-2-end approach to accessible computing. London: Springer London, 2015.

    Google Scholar 

  5. National Instruments, Fundamentals of Fiber Bragg Grating (FBG) Optical Sensing, 2016.

  6. Soto, M. A., Bolognini, G., Di Pasquale, F., and Thévenaz, L., Simplex-coded BOTDA fiber sensor with 1 m spatial resolution over a 50 km range. Opt. Lett. 35(2):259–261, 2010.

    Article  PubMed  Google Scholar 

  7. Terry, N., Will the Internet of Health Things Disrupt Healthcare?. SSRN Electron. J., pp. 28–31, 2016.

  8. Istepanian, R. S. H., Hu, S., Philip, N. Y., and Sungoor, A., “The potential of Internet of m-health Things “m-IoT” for non-invasive glucose level sensing,” in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011, pp. 5264–5266.

  9. Williams, P. A. H. and McCauley, V., Always connected: The security challenges of the healthcare Internet of Things in 2016 I.E. 3rd World Forum on Internet of Things (WF-IoT), 2016, pp. 30–35.

  10. Cavoukian, A., Privacy and drones : unmanned aerial vehicles. Priv. by Des. Canada 1(1):1–27, 2012.

    Google Scholar 

  11. Gong, Y., Fang, Y., and Guo, Y., Private Data Analytics on Biomedical Sensing Data via Distributed Computation. IEEE/ACM Trans. Comput. Biol. Bioinforma. 13(3):431–444, 2016.

    Article  Google Scholar 

  12. Liu, J., Shen, H., and Zhang, X, A survey of mobile crowdsensing techniques: A critical component for the internet of things. 2016 25th Int. Conf. Comput. Commun. Networks, ICCCN 2016, 2016.

  13. G. Chatzimilioudis, A. Konstantinidis, C. Laoudias, and D. Zeinalipour-yazti, “Crowdsourcing with Smartphones,” pp. 1–7, 2012.

  14. Chessa, S., Corradi, A., Foschini, L., and Girolami, M., Empowering mobile crowdsensing through social and ad hoc networking. IEEE Commun. Mag. 54(7):108–114, 2016.

    Article  Google Scholar 

  15. Carpenter, A., and Frontera, A., Smart-watches: a potential challenger to the implantable loop recorder? Europace 18(6):791–793, 2016.

    Article  PubMed  Google Scholar 

  16. Kos, A., Tomazic, S., and Umek, A, “Evaluation of smartphone inertial sensor performance for cross-platform mobile applications,” Sensors (Switzerland), vol. 16, no. 4, 2016.

  17. Dutta, J., Gazi, F., Roy, S., and Chowdhury, C., AirSense: Opportunistic crowd-sensing based air quality monitoring system for smart city,” in 2016 I.E. SENSORS, 2016, pp. 1–3.

  18. Van Hoe, B., Lee, G., Bosman, E., Missinne, J., Kalathimekkad, S., Maskery, O., Webb, D. J., Sugden, K., Van Daele, P., and Van Steenberge, G., Ultra small integrated optical fiber sensing system. Sensors (Switzerland) 12(9):12052–12069, 2012.

    Article  Google Scholar 

  19. Marika, P.and Di, D., “Sensors , Signal Processing & Applications,” vol. 5, no. 3, 2012.

  20. Majethia, R., Mishra, V., Pathak, P., Lohani, D., Acharya, D., and Sehrawat, S., “Contextual sensitivity of the ambient temperature sensor in Smartphones,” 2015 7th Int. Conf. Commun. Syst. Networks, COMSNETS 2015 - Proc., 2015.

  21. W. H. O. (WHO), “Who_Ncd_Ncs_99.2.Pdf,” 1999.

  22. Liu, S., Luo, Y., and Yang, S., Shape-based pedestrian detection in infrared images. J. Inf. Sci. Eng. 23(1):271–283, 2007.

    CAS  Google Scholar 

  23. H. Nanda and L. Davis, “Probabilistic template based pedestrian detection in infrared videos,” in Intell. Vehicle Sym., 2002. IEEE, vol. 1, pp. 15–20.

  24. Gidel, S., Checchin, P., Blanc, C., Chateau, T., Trassoudaine, L., and Pascal, U. B., Pedestrian Detection Method using a Multilayer Laserscanner : Application in Urban Environment pp. 22–26, 2008.

  25. Eccv, A., A heuristic deformable pedestrian detection method 542–543, 2011.

  26. Pesko, M., Smolnikar, M., Vucnik, M., Javornic, T., Pejanoviz-Djurisic, M., and Mohorcic, M., Smartphone with Augmented Gateway Functionality as Opportunistic WSN Gateway Device. Wirel. Pers. Commun. 78(3):1811–1826, 2014.

    Article  Google Scholar 

  27. Aloi, G., Caliciuri, G., Fortino, G., Gravina, R., Pace, P., Russo, W., and Savaglio, C., Enabling IoT interoperability through opportunistic smartphone-based mobile gateways. J. Netw. Comput. Appl. 81:74–84, 2017.

    Article  Google Scholar 

  28. Morón, M. J., Luque, R., and Casilari, E., On the capability of smartphones to perform as communication gateways in medical wireless personal area networks. Sensors (Switzerland) 14(1):575–594, 2014.

    Article  Google Scholar 

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Funding

This study received no funding. Funding grant will be applied for follow-up works.

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Authors and Affiliations

  1. Technical University of Munich, Institut für Informatik / I1, Boltzmannstraße 3, D-85748 Garching b, Munich, Germany

    Thierry Edoh

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  1. Thierry Edoh
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Correspondence to Thierry Edoh.

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Author Edoh declares that he has no conflict of interest.

Informed consent was obtained from all individual participants included in the study. (for details see section 3/subsection 3.3).

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This article is part of the Topical Collection on Mobile & Wireless Health

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Edoh, T. Risk Prevention of Spreading Emerging Infectious Diseases Using a HybridCrowdsensing Paradigm, Optical Sensors, and Smartphone. J Med Syst 42, 91 (2018). https://doi.org/10.1007/s10916-018-0937-2

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  • DOI: https://doi.org/10.1007/s10916-018-0937-2

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