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Stairstep recognition and counting in a serious Game for increasing users’ physical activity

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Abstract

The high diffusion of smartphones in the users’ pockets allows to sense their movements, thus monitoring the amount of physical activity they do during the day. But, it also gives the possibility to use these devices to persuade people to change their behaviors. In this paper, we present ClimbTheWorld, a serious game which uses a machine learning-based technique to recognize and count stairsteps and aims at persuading people to use stairs instead of elevators or escalators. We perform a fine-grained analysis by exploiting smartphone sensors to recognize single stairsteps. Energy consumption is widely investigated to avoid exhausting smartphone battery. Moreover, we present game appreciation and persuasive power results after a trial experiment with 13 participants.

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Notes

  1. http://www.nike.com/us/en_us/c/nikeplus-fuelband.

  2. We must note here that even if most of the commercial apps do not impose a fixed position to wear the smartphone, they often require an external device, which is even worst in terms of cost and intrusiveness of the system.

  3. In this case, we require the user to connect to Facebook and to give the application the right to explorer his/her network of friends.

  4. http://developer.android.com/guide/topics/sensors/sensors_overview.html.

  5. In this paper, we consider a device movement consistent with the body movement whenever the device movement is negligible relatively to the body’s barycenter.

  6. Our KOMD implementations in Python and R can be found at ,https://github.com/jmikko/EasyMKL.

  7. http://www.msoon.com/LabEquipment/PowerMonitor/.

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Acknowledgments

The authors would like to thank Nicola Beghin, Silvia Segato, and Mattia Bazzega for their contribution to the implementation of ClimbTheWorld. Moreover, the authors would like to thank the users who have participated in the user test, and those who helped to collect stairstep data.

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Authors and Affiliations

  1. Department of Mathematics, University of Padua, Via Trieste, 63, Padua, Italy

    Matteo Ciman, Michele Donini, Ombretta Gaggi & Fabio Aiolli

  2. Computational Statistics and Machine Learning, Istituto Italiano di Tecnologia, Genoa, Italy

    Michele Donini

Authors
  1. Matteo Ciman
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  2. Michele Donini
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  3. Ombretta Gaggi
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  4. Fabio Aiolli
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Correspondence to Matteo Ciman.

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Ciman, M., Donini, M., Gaggi, O. et al. Stairstep recognition and counting in a serious Game for increasing users’ physical activity. Pers Ubiquit Comput 20, 1015–1033 (2016). https://doi.org/10.1007/s00779-016-0968-y

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  • DOI: https://doi.org/10.1007/s00779-016-0968-y

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