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Activity Recognition Based on RFID Object Usage for Smart Mobile Devices

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Abstract

Activity recognition is a core aspect of ubiquitous computing applications. In order to deploy activity recognition systems in the real world, we need simple sensing systems with lightweight computational modules to accurately analyze sensed data. In this paper, we propose a simple method to recognize human activities using simple object information involved in activities. We apply activity theory for representing complex human activities and propose a penalized naive Bayes classifier for performing activity recognition. Our results show that our method reduces computation up to an order of magnitude in both learning and inference without penalizing accuracy, when compared to hidden Markov models and conditional random fields.

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Authors and Affiliations

  1. Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, U.S.A.

    Jaeyoung Yang

  2. Neowiz Internet Inc., Seoul, Korea

    Joonwhan Lee

  3. Department of Computer Science and Engineering, Hanyang University, Ansan, Gyeonggi-Do, 426–791, Korea

    Joongmin Choi

Authors
  1. Jaeyoung Yang
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  2. Joonwhan Lee
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  3. Joongmin Choi
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Corresponding author

Correspondence to Jaeyoung Yang.

Additional information

This work was supported by the Korea Research Foundation under Grant No. KRF-2008-357-D00221.

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Yang, J., Lee, J. & Choi, J. Activity Recognition Based on RFID Object Usage for Smart Mobile Devices. J. Comput. Sci. Technol. 26, 239–246 (2011). https://doi.org/10.1007/s11390-011-9430-9

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  • DOI: https://doi.org/10.1007/s11390-011-9430-9

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