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An Activity Recognition System For Mobile Phones

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Abstract

We present a novel system that recognizes and records the motional activities of a person using a mobile phone. Wireless sensors measuring the intensity of motions are attached to body parts of the user. Sensory data is collected by a mobile application that recognizes prelearnt activities in real-time. For efficient motion pattern recognition of gestures and postures, feed-forward backpropagation neural networks are adopted. The design and implementation of the system are presented along with the records of our experiences. Results show high recognition rates for distinguishing among six different motion patterns. The recognized activity can be used as an additional retrieval key in an extensive mobile memory recording and sharing project. Power consumption measurements of the wireless communication and the recognition algorithm are provided to characterize the resource requirements of the system.

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Acknowledgements

The authors would like to thank Michael Cohen and the reviewers of this paper for providing valuable insights and suggestions for improvement.

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Authors and Affiliations

  1. Spatial Media Group, University of Aizu, Aizu-Wakamatsu, Fukushima-ken, 965-8580, Japan

    Norbert Győrbíró

  2. Budapest University of Technology and Economics, Budapest, Hungary

    Ákos Fábián

  3. Nokia Siemens Networks, Köztelek utca 6., Budapest, 1092, Hungary

    Gergely Hományi

Authors
  1. Norbert Győrbíró
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  2. Ákos Fábián
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  3. Gergely Hományi
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Corresponding author

Correspondence to Norbert Győrbíró.

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Győrbíró, N., Fábián, Á. & Hományi, G. An Activity Recognition System For Mobile Phones. Mobile Netw Appl 14, 82–91 (2009). https://doi.org/10.1007/s11036-008-0112-y

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