A Simple Movement Classification System for Smartphones with Accelerometer

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 276)

Abstract

The penetration rate of smartphones has been growing during the past years. Today’s smartphones provide access to the Internet, GPS navigation and are equipped with cameras and various sensors: accelerometer, gyroscope, proximity sensor and light sensor among others. The main objective of this paper is to propose a movement classification system whose main characteristic is to obtain the numeric acceleration values along the three axes of the accelerometer and the subsequent conversion to one limited set of linguistic terms. The resulting simple classification of movements is sufficient to classify correctly (from a person’s point of view) the smartphone movements as well as their intensity. The validation tests and the proof of concept presented in this article open the path to the development of applications for physiotherapy and mobile health, especially those aimed at improving health and welfare through motivation for physical activity.

Keywords

Mobile Computing Smartphone Accelerometer mHealth 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Blog Strategy Analytics. Worldwide Smartphone Population Tops 1 Billion in Q3 (october 2012), http://blogs.strategyanalytics.com/WDS/post/2012/10/17/Worldwide-Smartphone-Population-Tops-1-Billion-in-Q3-2012.aspx (retrieved from)
  2. 2.
    GSMA Intelligence. Smartphone users spending more ‘face time’ on apps than voice calls or web browsing (2011), https://gsmaintelligence.com/analysis/2011/03/smartphone-users-spending-more-face-time-on-apps-than-voice-calls-or-web-browsing/271 (retrieved from)
  3. 3.
    GigaOM. 4 ways mobile health could save $400B in health costs (February 2013), http://gigaom.com/2013/02/25/4-ways-mobile-health-could-save-400b-in-health-costs/ (retrieved from)
  4. 4.
    Stankevich, E., Paramonov, I., Timofeev, I.: Mobile Phone Sensors in Health Applications. In: Proceeding of the 12th Conference of FRUCT Association, pp. 136–141 (April 2012)Google Scholar
  5. 5.
    Sposaro, F., Tyson, G.: iFall: an Android application for fall monitoring and response. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6119–6122 (2009)Google Scholar
  6. 6.
    Lockman, J., Fisher, R., Olson, D.: Detection of seizure-like movements using a wrist accelerometer. Epilepsy & Behavior 20(4), 638–641 (2011)CrossRefGoogle Scholar
  7. 7.
  8. 8.
  9. 9.
    BIOPAC Systems, Inc. Tri-Axial Accelerometers (November 2013), http://www.biopac.com/Product_Spec_PDF/Accelerometers.pdf (retrieved from)

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Departamento de InformáticaUniversidade da Beira InteriorCovilhãPortugal
  2. 2.Instituto de TelecomunicaçõesUniversidade da Beira InteriorCovilhãPortugal

Personalised recommendations