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Monitoring Physical Activities Using Smartphones

  • Pablo Romo
  • Sergio F. Ochoa
  • Nelson Baloian
  • Ignacio Casas
  • José Bravo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8867)

Abstract

It is well-known that physical activities contribute to keep the people healthy. However, the modern life style impacts negatively on the amount of physical activity that we do during the day. Many times the people do not perform enough exercise because they are not aware of the amount of physical activity that they have done. In order to make the persons aware of this aspect of his life, this article presents a mobile application that monitors the amount of exercise they do every day and it informs properly to the user. The system, named AMOPA, allows caregivers or doctors monitoring particular patients, to access this information remotely in order to support the person being monitored. The system was evaluated using laboratory tests, and the results indicate a good performance and accuracy in the detection of the people physical activities. Moreover, the monitored process has a low impact on the energy consumption of the devices used to capture and process the information.

Keywords

Monitoring of physical activities healthcare model lifestyle urban population 

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References

  1. 1.
    Bernstein, M.S., Morabia, A., Sloutskis, D.: Definition and prevalence of sedentarism in an urban population. American J. Public Health 89(6), 862–867 (1999)CrossRefGoogle Scholar
  2. 2.
    Chilean National Institute of Sports, National Study on Sports and Healthy Habits on People over 18 Years Old (in Spanish) (2010), http://www.ind.cl/estudios-e-investigacion/investigaciones/Documents/2012/encuesta_nacional_habitos.pdf
  3. 3.
    Guo, Q.: Android Health-Care App: Multi-function Step Counter. Master Thesis, Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media (2012)Google Scholar
  4. 4.
    Oner, M., Pulcifer-Stump, J., Seeling, P., Kaya, T.: Towards the Run and Walk Activity Classification through Step Detection: An Android Application. In: Proceedings of the 34th Annual International Conference of the Engineering in Medicine and Biology Society, pp. 1980–1983 (2012)Google Scholar
  5. 5.
    Shin, J., Shin, D., Shin, D., Her, S., Kim, S., Lee, M.: Human Movement Detection Algorithm Using 3-Axis Accelerometer Sensor Based on Low-Power Management Scheme for Mobile Health Care System. In: Bellavista, P., Chang, R.-S., Chao, H.-C., Lin, S.-F., Sloot, P.M.A. (eds.) GPC 2010. LNCS, vol. 6104, pp. 81–90. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Ferreira, D., Dey, A.K., Kostakos, V.: Understanding Human-Smartphone Concerns: A Study of Battery Life. In: Lyons, K., Hightower, J., Huang, E.M. (eds.) Pervasive 2011. LNCS, vol. 6696, pp. 19–33. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Muller, M.: Dynamic Time Warping. In: Information Retrieval for Music and Motion, ch. 4, pp. 69–84. Springer (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Pablo Romo
    • 1
  • Sergio F. Ochoa
    • 1
  • Nelson Baloian
    • 1
  • Ignacio Casas
    • 2
  • José Bravo
    • 3
  1. 1.Computer Science DepartmentUniversity of ChileSantiagoChile
  2. 2.Computer Science DepartmentPontifical Catholic University of ChileSantiagoChile
  3. 3.Castilla-La Mancha UniversityCiudad RealSpain

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