Personal and Ubiquitous Computing

, Volume 15, Issue 7, pp 667–678 | Cite as

Accurate monitoring of human physical activity levels for medical diagnosis and monitoring using off-the-shelf cellular handsets

  • Martin Hynes
  • Han Wang
  • Eleanor McCarrick
  • Liam Kilmartin
Original Article


Numerous laboratory-based studies have been reported on the use of accelerometry for gait and activity analysis particularly among cohorts of elderly subjects. A drawback of such studies is the use of custom hardware platforms worn by subjects. This paper introduces a system solely utilizing accelerometers embedded in off-the-shelf cellular handsets that allow medical professionals and caregivers to remotely monitor the activity characteristics of elderly patients in the home or in the community. The use of ubiquitous cellular handsets makes the system far more acceptable to patients and enables the use of the system to be extended beyond healthcare facilities into the home environment. Mobile handset power consumption issues and other relevant handset and mobile handset application characteristics have been investigated in the context of the deployment of the proposed system.


Healthcare Mobile computing Pervasive computing 



This work was supported in part by a grant from Enterprise Ireland under their Commercialization Fund—Proof of Concept Phase Programme (PC/2008/0120). Additional support for the research was provided by O2-Telefonica (Ireland).


  1. 1.
    Ibrahim RK, Ambikairajah E, Celler B, Lovell NH, Kilmartin L (2008) Gait patterns classification using spectral features. In: Proceedings of the IET Irish signals and systems conference, June 2008, pp 98–102Google Scholar
  2. 2.
    Han JH, Jeon HS, Park KS (2008) Gait detection from three dimensional acceleration signals of ankles for patients with Parkinson’s disease. In: Proceedings of the international conference on technology and applications in biomedicine, ITAB 2008, May 2006, pp 349–352Google Scholar
  3. 3.
    Yoshida T, Mizuno F, Hayasaka T, Tsubota K, Wada S, Yamaguchi T (2006) Gait analysis for detecting a leg a accident with an accelerometer. In: Proceedings of the transdisciplinary conference on distributed diagnosis and home healthcareGoogle Scholar
  4. 4.
    Slawson SE, Justham LM, West PP, Conway MP, Caine MP, Harrison R (2008) Accelerometer profile recognition of swimming strokes. The engineering of sport 7. Springer, Paris, pp 81–87Google Scholar
  5. 5.
    DeLisa JA, Casey K (1998) Gait analysis in the science of rehabilitation. DIANE Publishing, DarbyGoogle Scholar
  6. 6.
    Iso T, Yamazaki K (2006) Gait analyzer based on a cell phone with a single three-axis accelerometer. In: MobileHCI '06: Proceedings of the 8th conference on Human-computer interaction with mobile devices and services, pp 141–144Google Scholar
  7. 7.
    Annavaram M, Medvidovic N, Mitra U, Narayanan S, Sukhatme G, Meng Z, Qiu S, Kumar R, Thatte G, Spruijt-Metz D (2008) Multimodal Sensing of pediatric obesity applications. In: Proceedings of urbansense 2008, Nov 2008, pp 21–25Google Scholar
  8. 8.
    Mladenov M, Mock M (2009) A step counter service for java-enabled devices using a built-in accelerometer. In: Proceedings of the 1st international workshop on context-aware middleware and services: affiliated with the 4th international conference on communication system software and middleware (COMSWARE 2009), June 2009Google Scholar
  9. 9.
    Vajk T, Bamford W, Coulton P, Edwards R (2007) Using a mobile phone as a ‘Wii like’ controller. In: Proceedings of cybergames 2007, Sep 2007Google Scholar
  10. 10.
    Arensman WL, Whipple JG, Boler MS (2009) A public safety application of GPS-enabled smartphones and the android operating system. In: Proceedings of systems, man and cybernetics (SMC 2009), Oct 2009Google Scholar
  11. 11.
    Chen KY, Acra SA, Majchrzak K, Donahue CL, Baker L, Clemens L, Sum M, Buchowski MS (2003) Predicting energy expenditure of physical activity using hip- and wrist-worn accelerometers. Diabetes Technol Ther 5(6):1023–1033Google Scholar
  12. 12.
    Niezen G, Hancke GP (2008) Gesture recognition as ubiquitous input for mobile phones. In: Proceedings of ubiquitous computing (UbiComp 2008), Sep 2008Google Scholar
  13. 13.
    Gimeno J, Morillo P, Coma I, Fernández M (2009) A Device-independent 3D user interface for mobile phones based on motion and tracking. In: Proceedings of the 2009 International conference on image processing, computer vision and pattern recognition (IPCV 2009), July 2009Google Scholar
  14. 14.
    Kilmartin L, Ibrahim RK, Ambikairajah E, Celler B (2009) Optimising recognition rates for subject independent gait pattern classification. In: Proceedings of the IET Irish signals and systems conference 2009, June 2009Google Scholar

Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  • Martin Hynes
    • 1
  • Han Wang
    • 1
  • Eleanor McCarrick
    • 2
  • Liam Kilmartin
    • 1
  1. 1.School of Engineering and Informatics, NUIGalwayIreland
  2. 2.School of Medicine, NUIGalwayIreland

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