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A Proposal for a Dynamic Digital Map to Prevent Heatstroke Using IoT Data

  • Kanae MatsuiEmail author
  • Keiya Sakai
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 926)

Abstract

This paper presents a dynamic digital map displaying user’s position and his/her environmental and health status for prevention of heatstroke, which is one of worst natural disaster in Japan. We use Internet of Things (IoT) data of Global Positioning System (GPS) and temperature surrounding people. Victims of heatstroke in Japan have been increasing in summer, and the heatstroke causes a large number of deaths annually. To avoid this situation when people are walking outside in summer, our proposed digital map provides the information of their walking locus created by their GPS data along with their heartbeat rate and surrounding temperature collected by IoT devices. To develop our proposed system, we conducted an experiment, and the results of the dynamic digital map are promising.

Notes

Acknowledgements

This work was supported in part by the R&D project “Design of Information and Communication Platform for Future Smart Community Services” by the Ministry of Internal Affairs and Communications (MIC) of Japan, and was supported by Funds for the Integrated Promotion of Social System Reform and Research and Development, MEXT, Japan, and by MEXT/JSPS KAKENHI Grant (B) Number 17K12789 and SECOM Science and Technology Foundation.

References

  1. 1.
    Ministry of Health, Labour and Welfare: “Number of deaths due to heat stroke (demographics statistics).” https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000121413.html. Accessed 25 Nov 2018. (Japanese Only)
  2. 2.
    Ministry of Internal Affairs and Communications: “Emergency transportation situation due to heat stroke.” http://www.fdma.go.jp/neuter/topics/houdou/h29 /10/291018_houdou_3.pdf. Accessed 25 Nov 2018. (Japanese Only)
  3. 3.
    Japan Meteorological Agency: “Call for attention of heat stroke in abnormal weather early warning information.” https://www.jma.go.jp/jma/kishou/know/kurashi/soukei_netsu.html. Accessed 25 Nov 2018. (Japanese Only)
  4. 4.
    Jun, M., Fumiaki, F., Hideo, T.: Urban climate in the Tokyo metropolitan area in Japan. J. Environ. Sci. 59, 54–62 (2017)CrossRefGoogle Scholar
  5. 5.
    Google: Google Maps. https://www.google.com/maps. Accessed 25 Nov 2018
  6. 6.
    Yahoo Japan Corporation: “Yahoo!Map (Japanese ver). https://map.yahoo.co.jp/. Accessed 25 Nov 2018
  7. 7.
    Zanella, A., et al.: Internet of things for smart cities. IEEE Internet Things J. 1(1), 22–32 (2014)CrossRefGoogle Scholar
  8. 8.
    Sakhardande, P., Hanagal, S., Kulkarni, S.: Design of disaster management system using IoT based interconnected network with smart city monitoring. In: International Conference on Internet of Things and Applications (IOTA), pp. 185–190. IEEE (2016)Google Scholar
  9. 9.
    Mcmahon, D., Don, D., et al.: Effects of digital navigation aids on adults with intellectual disabilities: comparison of paper map, Google maps, and augmented reality. J. Spec. Educ. Technol. 30(3), 157–165 (2015)CrossRefGoogle Scholar
  10. 10.
    Gerla, M., et al.: Internet of vehicles: from intelligent grid to autonomous cars and vehicular clouds. In: 2014 IEEE World Forum on Internet of Things (WF-IoT), pp. 241–246. IEEE (2014)Google Scholar
  11. 11.
    TripAdvisor, Inc.: TripAdvisor (Japanese ver). https://www.tripadvisor.jp/. Accessed 25 Nov 2018
  12. 12.
    Yelp, Inc.: yelp. https://www.yelp.com/. Accessed 25 Nov 2018
  13. 13.
    Xie, K., et al.: Use of real-world connected vehicle data in identifying high-risk locations based on a new surrogate safety measure. Accid. Anal. Prev. (2018).  https://doi.org/10.1016/j.aap.2018.07.002
  14. 14.
    Uber Technologies Inc.: Uber. https://www.uber.com/. Accessed 25 Nov 2018
  15. 15.
    Lyft, Inc.: Lyft. https://www.lyft.com/. Accessed 25 Nov 2018
  16. 16.
    Koopman, C., Mitchell, M., Thierer, A.: The sharing economy and consumer protection regulation: the case for policy change. J. Bus. Entrepreneurship L. 8, 529 (2014)Google Scholar
  17. 17.
    Uber Engineering: “kepker.” https://kepler.gl/. Accessed 25 Nov 2018
  18. 18.
    Gomes, G.A., Santos, E., Vidal, C.A.: Interactive visualization of traffic dynamics based on trajectory data. In: 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 111–118. IEEE (2017)Google Scholar
  19. 19.
    Internet Engineering Task Force (IETF): “The OAuth 2.0 Authorization Framework.” https://tools.ietf.org/html/rfc6749. Accessed 25 Nov 2018
  20. 20.
    Scargle, J.D.: Studies in astronomical time series analysis. II-Statistical aspects of spectral analysis of unevenly spaced data. Astrophys. J. 263, 835–853 (1982)CrossRefGoogle Scholar
  21. 21.
    Google: Google Fusion Tables. https://fusiontables.google.com/. Accessed 25 Nov 2018
  22. 22.
    Ministry of the Environment: “Heat stroke prevention information site.” https://fusiontables.google.com/. Accessed 25 Nov 2018
  23. 23.
    Google Maps Platform: “Roads API.” https://developers.google.com/maps/documentation /roads/intro. Accessed 25 Nov 2018
  24. 24.
    Wu, J., et al.: Fast complementary filter for attitude estimation using low-cost MARG sensors. IEEE Sens. J. 16(18), 6997–7007 (2016)CrossRefGoogle Scholar
  25. 25.
    Budd, G.M.: Wet-bulb globe temperature (WBGT)—its history and its limitations. J. Sci. Med. Sport. 11(1), 20–32 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Tokyo Denki UniversityAdachi-kuJapan

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