Skip to main content
SpringerLink
Log in

Shakra: Tracking and Sharing Daily Activity Levels with Unaugmented Mobile Phones

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

This paper explores the potential for use of an unaugmented commodity technology—the mobile phone—as a health promotion tool. We describe a prototype application that tracks the daily exercise activities of people, using an Artificial Neural Network (ANN) to analyse GSM cell signal strength and visibility to estimate a user’s movement. In a short-term study of the prototype that shared activity information amongst groups of friends, we found that awareness encouraged reflection on, and increased motivation for, daily activity. The study raised concerns regarding the reliability of ANN-facilitated activity detection in the ‘real world’. We describe some of the details of the pilot study and introduce a promising new approach to activity detection that has been developed in response to some of the issues raised by the pilot study, involving Hidden Markov Models (HMM), task modelling and unsupervised calibration. We conclude with our intended plans to develop the system further in order to carry out a longer-term clinical trial.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Adams J, White M (2005) Why don’t stage-based activity promotion interventions work? Health Educ Res 20(No. 2):237–243

    Article  Google Scholar 

  2. Anderson I, Muller H (2006) Context awareness via GSM signal strength fluctuation. In: The 4th international conference on pervasive computing, late breaking results. Oesterreichische Computer Gesellschaft, pp 27–31 May

  3. Baum LE, Peterie T, Souled G, Weiss N (1970) A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Ann Math Stat 41(1):164–171

    Article  MATH  Google Scholar 

  4. Bell M, et al. (2006) Interweaving mobile games with everyday life. Proc ACM CHI, pp 417–426 (Also available at http://www.dcs.gla.ac.uk/~matthew/papers/CHIYoshiWithCopyright.pdf)

  5. Consolvo S, Everitt K, Smith I, Landay JA (2006) Design requirements for technologies that encourage physical activity. Proc ACM CHI: pp 457–466

  6. Department of Health (2004) At least five a week: evidence on the impact of physical activity and its relationship to health. DoH, London

  7. Duncan SC, Duncan TE, Strycker LA, Chaumeton NR (2004) A multilevel approach to youth physical activity research. Exerc Sport Sci Rev 32(No.3):95–99

    Article  Google Scholar 

  8. Ipsos Mori, e-MORI Technology Tracker, December 2002 http://www.mori.com/technology/techtracker.shtml [last accessed 29th June 2006]

  9. King AC, Friedman R, Marcus B, Castro C, Forsyth L, Napolitano M, Pinto B (2002) Harnessing motivational forces in the promotion of physical activity: the Community Health Advice by Telephone (CHAT) Project. Health Educ Res 17(No. 5):627–636

    Article  Google Scholar 

  10. Korp P (2006) Health on the internet: implications for health promotion. Health Educ Res 21(No. 1):78–86

    Article  Google Scholar 

  11. Lester J, Choudhury T, Borriello G, Consolvo S, Landay J, Everitt K, Smith, I. Sensing and modeling activities to support physical fitness. Workshop paper in UbiComp ’05 Workshop: Monitoring, Measuring and Motivating Exercise: Ubiquitous Computing to Support Physical Fitness. Tokyo, Japan, 2005

  12. Licoppe C, Inada Y (2006) Emergent uses of a Multiplayer Location—Aware Mobile Game: the Interactional Consequences of Mediated Encounters. Mobilities 1(No. 1)

  13. McLean N, Griffin S, Toney K, Hardeman W (2003) Family involvement in weight control, weight maintenance and weight-loss interventions: a systematic review of randomised trials. Int J Obes 27:987–1005

    Article  Google Scholar 

  14. Mullen E, Markland D (1997) Variations in self-determination across the stages of change for exercise in adults. Motiv Emot 21(No. 4):349–362

    Article  Google Scholar 

  15. Pate RR, Pratt M, Blair SN, Haskell WL, Macera CA, Bouchard C (1995) Physical activity and public health: a recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine. J Am Med Assoc 273:402–407

    Article  Google Scholar 

  16. Rabiner LR (1990) A tutorial on hidden Markov models and selected applications in speech recognition. In Readings in speech recognition. Morgan Kaufmann, San Francisco, CA, USA, pp 267–296

  17. Siewiorek DP, Smailagic A, Furukawa J, Krause A, Moraveji N, Reiger K, Shaffer J, Wong FL (2003) SenSay: A Context-Aware Mobile Phone. In Proceedings of 7th International Symposium on Wearable Computers, ISWC. IEEE Computer Society, pp 248–249

  18. Sparling PB, Owen N, Lambert EV, Haskell WL (2000) Promoting physical activity: the new imperative for public health. Health Educ Res 15(No. 3):367–376

    Article  Google Scholar 

  19. Speck BJ, Harrell JS (2003) Maintaining regular physical activity in women—evidence to date. J Cardiovasc Nurs 18(No. 4):282–291

    Google Scholar 

  20. Tudor-Locke C (2002) Taking Steps Toward Increased Physical Activity: Using Pedometers to Measure and Motivate. President’s Council on Physical Fitness and Sports, June

  21. Vandelanotte C, De Bourdeaudhuij I (2003) Acceptability and feasibility of a computer-tailored physical activity intervention using stages of change: project FAITH. Health Educ Res 18(No. 3):304–317

    Article  Google Scholar 

  22. World Health Organisation: Move for Health http://www.who.int/moveforhealth/en/ [last accessed 29th June 2006]

  23. Viterbi AJ (1967) Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans Inf Theory 13(2):260–269

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Bristol, BS8 1UB, Bristol, UK

    Ian Anderson & Henk Muller

  2. Department of Computer Science, University of Glasgow, G12 8QQ, Glasgow, UK

    Julie Maitland, Scott Sherwood, Louise Barkhuus, Matthew Chalmers, Malcolm Hall & Barry Brown

Authors
  1. Ian Anderson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Julie Maitland
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Scott Sherwood
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Louise Barkhuus
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Matthew Chalmers
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Malcolm Hall
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Barry Brown
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Henk Muller
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ian Anderson.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Anderson, I., Maitland, J., Sherwood, S. et al. Shakra: Tracking and Sharing Daily Activity Levels with Unaugmented Mobile Phones. Mobile Netw Appl 12, 185–199 (2007). https://doi.org/10.1007/s11036-007-0011-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-007-0011-7

Keywords

Search

Navigation