Implicit Interaction: A Modality for Ambient Exercise Monitoring

  • J. Wan
  • M. J. O’Grady
  • G. M. P. O’Hare
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5727)


Ambient Exercise refers to the implicit exercise that people undertake in the course of their everyday duties - a simple example being climbing stairs. Increasing awareness of the potential health benefits of such activities may well contribute to an increase in a person’s well-being. Initially, it is necessary to monitor and quantify such exercise so that personalized fitness plans may be constructed. In this paper, the implicit interaction modality is harnessed to enable the capturing of ambient exercise activity thereby facilitating its subsequent quantification and interpretation. The novelty of the solution proposed lies in its ubiquity and transparency.


Ambient exercise Implicit interaction Pervasive health 


  1. 1.
    O’Grady, M.J., O’Hare, G.M.P., Keegan, S.: Interaction Modalities in Mobile Contexts. In: Virvou, M., Jain, L. (eds.) Studies in Computational Intelligence (SCI), vol. 104, pp. 89–106 (2008)Google Scholar
  2. 2.
    Tsai, C.C., Lee, G., Raab, F., Norman, G.J., Sohn, T., Griswold, W.G., Patrick, K.: Usability and feasibility of PmEB: a mobile phone application for monitoring real time caloric balance. Mob. Netw. Appl. 12(2-3), 173–184 (2007)CrossRefGoogle Scholar
  3. 3.
    Anderson, I., Maitland, J., Sherwood, S., Barkhuus, L., Chalmers, M., Hall, M., Brown, B., Muller, H.: Shakra: Tracking and Sharing Daily Activity Levels with Unaugmented Mobile Phones. Springer Science + Business Media (2007)Google Scholar
  4. 4.
    Oliver, N., Flores-Mangas, F.: HealthGear: A Real-time Wearable System for Monitoring and Analyzing Physiological Signals. In: International Workshop on Wearable and Implantable Body Sensor Networks, pp. 61–64 (2006)Google Scholar
  5. 5.
    Buttussi, A., Chittaro, L.: MOPET: A context-aware and user-adaptive wearable system for fitness training. Artificial Intelligence in Medicine 42(2), 153–163 (2008)CrossRefGoogle Scholar
  6. 6.
    Ermes, M., Parkka, J., Mantyjarvi, J., Korhonen, I.: Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Transactions on Information Technology in Biomedicine 12(1), 20–26 (2006)CrossRefGoogle Scholar
  7. 7.
    O’Hare, G.M.P., O’Grady, M.: Addressing mobile HCI needs through agents. In: Paternó, F. (ed.) Mobile HCI 2002. LNCS, vol. 2411, pp. 311–314. Springer, Heidelberg (2002)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • J. Wan
    • 1
  • M. J. O’Grady
    • 2
  • G. M. P. O’Hare
    • 2
  1. 1.School of Computer Science & InformaticsUniversity College Dublin (UCD)Dublin 4Ireland
  2. 2.CLARITY: Centre for Sensor WWW TechnologiesUniversity College Dublin (UCD)Dublin 4Ireland

Personalised recommendations