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SCHAS: A Visual Evaluation Framework for Mobile Data Analysis of Individual Exposure to Environmental Risk Factors

  • Shayma Alkobaisi
  • Wan D. BaeEmail author
  • Sada Narayanappa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9239)

Abstract

Exposure to environmental risk factors as well as weather conditions are known to have negative effects on health. Until recently, there was little a society could do for an individual at risk, other than provide general warnings when the concentration of pollutants or weather conditions deviate from the norm. Similarly, the assessment of individuals’ exposure over time has been confined to population and geographic averages, rather than individualized estimates. Recent advances in sensors and mobile technology have enabled real-time measurements of environmental variables and, at the same time, provided information about the spatio-temporal behavior of individuals. This can dramatically change the way health and wellness are assessed as well as how care and treatment are delivered. This paper presents a system framework called “Smart and Connected Health Alert System (SCHAS)” for individual-level environmental exposure in an attempt to better understand the relationships among exposures, symptoms and human health conditions. We demonstrate user interface, data acquisition and visual evaluation tools for large mobile sensor data analysis.

Keywords

Mobile Device Mobile Sensor Individual Exposure Visual Analysis Tool Standard Module Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Centers for disease control and prevention. http://www.cdc.gov/asthma. Accessed June 2015
  2. 2.
    Icedot. https://icedot.org/. Accessed June 2015
  3. 3.
    Open layer. http://openlayers.org/. Accessed June 2015
  4. 4.
    Openweather map. http://openweathermap.org/. Accessed June 2015
  5. 5.
    Propeller health. http://www.propellerhealth.com/. Accessed June 2015
  6. 6.
    Trends in asthma morbidity and mortality. American Lung Association Epidemiology and Statistics and Health Education Division, September 2012Google Scholar
  7. 7.
    Alkobaisi, S., Vojtěchovský, P., Bae, W.D., Kim, S.H., Leutenegger, S.T.: The truncated tornado in tmbb: a spatiotemporal uncertainty model for moving objects. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds.) DEXA 2008. LNCS, vol. 5181, pp. 33–40. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  8. 8.
    Bae, W.D., Alkobaisi, S., Narayanappa, S., Liu, C.C.: A real-time health monitoring system for evaluating environmental exposures. Int. J. Softw. 8(4), 791–801 (2013)Google Scholar
  9. 9.
    Bae, W.D., Narayanappa, S., Alkobaisi, S., Bae, K.Y.: Mobis: a distributed paradigm of mobile sensor data analytics for evaluating environmental exposures. In: 10th ACM SIGSPATIAL Geographic Information Systems MobiGIS, pp. 93–96 (2012)Google Scholar
  10. 10.
    Gerharz, L., Pebesma, E.: Using geostatistical simulation to disaggregate air quality model results for individual exposure estimation on gps tracks. Stoch. Environ. Res. Risk Assess. 27(1), 223–234 (2013)CrossRefGoogle Scholar
  11. 11.
    Gibson, J.M., Brammer, A., Davidson, C., Folley, T., Launay, F., Thomsen, J.: Environmental Burden of Disease Assessment. Environmental Science and Technology Library, vol. 24. Springer, Netherlands (2013) Google Scholar
  12. 12.
    Gotway, C.A., Young, L.J.: Combining incompatible spatial data. J. Am. Stat. Assoc. 97(458), 632–648 (2002)zbMATHMathSciNetCrossRefGoogle Scholar
  13. 13.
    Miller, G.W., Jones, D.P.: The nature of nature: refining the definition of the exposome. Toxicol. Sci. 137(1), 1–2 (2014)CrossRefGoogle Scholar
  14. 14.
    Newhouse, C.P., Levetin, E.: Correlation of environmental factors with asthma and rhinitis symptoms in tulsa, ok. Ann. Assoc. Am. Geogr. 92(3), 356–366 (2004)zbMATHGoogle Scholar
  15. 15.
    Peters, A., Hoek, G., Katsouyanni, K.: Understanding the link between environmental exposures and health: does the exposome promise too much? J. Epidemiol. Community Health 66(2), 103–105 (2012)CrossRefGoogle Scholar
  16. 16.
    Schappert, S., Rechtsteiner, E.: Ambulatory medical care utilization estimates for 2007. Vital Health Stat. Ser. 13, Data Nat. Health Surv. 169, 1–38 (2011)Google Scholar
  17. 17.
    Seto, E.Y., Giani, A., Shia, V., Wang, C., Yan, P., Yang, A.Y., Jerrett, M., Bajcsy, R.: A wireless body sensor network for the prevention and management of asthma. In: IEEE International Symposium on Industrial Embedded Systems, pp. 120–123 (2009)Google Scholar
  18. 18.
    Trajcevski, G., Wolfson, O., Hinrichs, K., Chamberlain, S.: Managing uncertainty in moving objects databases. ACM Trans. Database Syst. 29(3), 463–507 (2004)CrossRefGoogle Scholar
  19. 19.
    Wild, C.P.: Exposome: from concept to utility. J. Epidemiol. 41(1), 24–32 (2012)MathSciNetGoogle Scholar
  20. 20.
    Zheng, K., Trajcevski, G., Zhou, X., Scheuermann, P.: Probabilistic range queries for uncertain trajectories on road networks. In: Proceedings of the 14th International Conference on Extending Database Technology, pp. 283–294. ACM (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Shayma Alkobaisi
    • 1
  • Wan D. Bae
    • 2
    Email author
  • Sada Narayanappa
    • 3
  1. 1.College of Information TechnologyUnited Arab Emirates UniversityAl AinUAE
  2. 2.Mathematics, Statistics and Computer ScienceUniversity of Wisconsin-StoutMenomonieUSA
  3. 3.MicrosoftNew YorkUSA

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