Using Smartphone Bases Biodevices for Analyzing Physiological, Psychological and Behavioral User’s Habits

Federico Guede Fernández, Marc Pous Solà, Miguel Ángel García González, Lluís Capdevila Ortís, Juan Ramos Castro, Mireya Fernández Chimeno

Abstract

As a consequence of increasing life expectancy, the promotion of lifestyles that allow aging wellbeing guarantees has acquired great importance in the developed countries. However, the adherence to healthy behaviors in young and adult people remains as a big problem in the community health field. The development of markers of adherence to healthy lifestyles and the evaluation its effectiveness is a goal of many research groups. This paper presents a system for analyzing physiological, psychological and behavioural user’s habits using a smartphone and externals biodevices. We use an Android smartphone with an internal tri-axial accelerometer and GPS to monitor physical activity. The smartphone is connected via Bluetooth to a respiratory sensor for breath monitoring. In addition, Android application contains psychological questionnaires to analyze user’s mood state and at the same, social interaction is analyzed tracking phone usage and user’s social network. Finally, the collected information is sent to a remote server for a long-term processing.

References

  1. 4ViewSoft. (2012). AChartEngine: Charting Library for Android (Version 1.0.0) [Software]. Available from http://code.google.com/p/achartengine/downloads/list
  2. Andrade, E., Arce, C., Torrado, J., Garrido, J., De Francisco, C., and Arce, I., (2010). Factor structure and invariance of the POMS mood state questionnaire in spanish. Spanish Journal of Psychology, 13(1), 444- 452.
  3. Billieux, J., (2012). Problematic use of the mobile phone: A literature review and a pathways model. Current Psychiatry Reviews, 8(4), 299-307. doi: 10.2174/157340012803520522.
  4. Brezmes, T., Gorricho, J. L. and Cotrina J., (2009). Activity recognition from accelerometer data on a mobile phone. In Proceedings of the IWANN 7809, 796- 799.
  5. Canalys, (2012). Smart phones overtake client PCs in 2011. Retrieved February 3, 2012, from http://www.canalys.com/newsroom/smart-phonesovertake-client-pcs-2011.
  6. comScore, (2012). Number of european smartphone users accessing news surges 74 percent over past year. Retrieved March 22, 2012, from http://www.comscore.com/Insights/Press_Releases/20 12/3/Number_of_European_Smartphone_Users_Acces sing_News_Surges_74_Percent_Over_Past_Year.
  7. Facebook, (2012). Android Tutorial. Retreived May 18, 2012, from https://developers.facebook.com/ docs/mobile/android/build/
  8. Gartner. (2012). Gartner says worldwide sales of mobile phones declined 2 percent in first quarter of 2012; previous year-over-year decline occurred in second quarter of 2009. Retrieved May 16, 2012, from http://www.gartner.com/it/page.jsp?id=2017015.
  9. Google Inc. (2012a). Android developer guide: Layouts. Retrieved May 11, 2012, from http://developer.android.com/guide/topics/ui/declaring -layout.html.
  10. Google Inc. (2012b). Google Maps Android API - External Library. Retreived June 1, 2012, from https://developers.google.com/maps/documentation/an droid/index
  11. Gramlich, N. (2012). OSMDroid: OpenStreetMap-Tools for Android (Version 3.0.8) [Software]. Avaliable from http://code.google.com/p/osmdroid/
  12. Holt-Lunstad, J., Smith, T. B., & Layton, J. B., (2010). Social relationships and mortality risk: A metaanalytic review. PLoS Medicine, 7(7) doi: 10.1371/journal.pmed.1000316
  13. Jarvinen, J., DeSalas, J. & LaMance, J., (2002). Assisted GPS: A Low-Infrastructure Approach. Retrieved March 1, 2002, from http://www.gpsworld.com/wpcontent/uploads/2012/09/gpsworld_Innovation_0302.p df
  14. McNair, D., (1984). Citation classic - manual for the profile of mood states. Current Contents/social & Behavioral Sciences, (27), 20-20.
  15. Ohida, T., Kamal, A. M. M., Uchiyama, M., Kim, K., Takemura, S., Sone, T and Ishii, T., (2001) The influence of lifestyle and health status factors on sleep loss among the Japanese general population. SLEEP, 24(3), 333-338.
  16. Ramos-Castro, J., Moreno, J., Miranda-Vidal, H., GarcíaGonzález, M .A., Fernández-Chimeno, M., Rodas, G. & Capdevila, Ll., (2012). Heart Rate Variability analysis using a Seismocardiogram signal. In Engineering in Medicine and Biology Society, EMBC 2012. Accepted.
  17. Rodriguez-Ibanez, N., Garcia-Gonzalez, M. A., Fernandez-Chimeno, M., & Ramos-Castro, J., (2011). Drowsiness detection by thoracic effort signal analysis in real driving environments. 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011, Boston, MA. 6055-6058. doi: 10.1109/IEMBS.2011.6091496
  18. Shin, J., Shin, D., Shin, D., Her, S., Kim, S., & Lee, M., (2010). Human movement detection algorithm using 3- axis accelerometer sensor based on low-power management scheme for mobile health care system (Hualien ed.) doi: 10.1007/978-3-642-13067-0_12
  19. Sposaro, F., & Tyson, G., (2009). iFall: An android application for fall monitoring and response. 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009, Minneapolis, MN. 6119-6122. doi: 10.1109/IEMBS.2009.5334912
  20. Thomée, S., Härenstam, A. & Hagberg, M., (2011). Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults - A prospective cohort study. BMC Public Health, 11 doi: 10.1186/1471-2458-11-66
  21. Yamamoto, Y., (2011). Twitter4j: an open-sourced Java library for the Twitter API. (Version 2.0.4) Avaliable from http://twitter4j.org/en/index.html
  22. Yang, S. & Gerla, M., (2011). Personal gateway in mobile health monitoring. 2011 9th IEEE International Conference on Pervasive Computing and Communications Workshops, PERCOM Workshops 2011, Seattle, WA. 636-641. doi: 10.1109/PERCOMW.2011.5766966
  23. Yuanchao, M., Bin, X. Yin, B., Guodong, S. and Run, Zhu, (2012). Daily Mood Assessment Based on Mobile Phone Sensing. In Wearable and Implantable Body Sensor Networks, 2012 Ninth International Conference on, 142-147.
Download


Paper Citation


in Harvard Style

Guede Fernández F., Solà M., García González M., Capdevila Ortís L., Ramos Castro J. and Fernández Chimeno M. (2013). Using Smartphone Bases Biodevices for Analyzing Physiological, Psychological and Behavioral User’s Habits . In Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2013) ISBN 978-989-8565-34-1, pages 243-248. DOI: 10.5220/0004206802430248


in Bibtex Style

@conference{biodevices13,
author={Federico Guede Fernández and Marc Pous Solà and Miguel Ángel García González and Lluís Capdevila Ortís and Juan Ramos Castro and Mireya Fernández Chimeno},
title={Using Smartphone Bases Biodevices for Analyzing Physiological, Psychological and Behavioral User’s Habits},
booktitle={Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2013)},
year={2013},
pages={243-248},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004206802430248},
isbn={978-989-8565-34-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2013)
TI - Using Smartphone Bases Biodevices for Analyzing Physiological, Psychological and Behavioral User’s Habits
SN - 978-989-8565-34-1
AU - Guede Fernández F.
AU - Solà M.
AU - García González M.
AU - Capdevila Ortís L.
AU - Ramos Castro J.
AU - Fernández Chimeno M.
PY - 2013
SP - 243
EP - 248
DO - 10.5220/0004206802430248