›› 2011, Vol. 26 ›› Issue (2): 239-246.doi: 10.1007/s11390-011-1126-7

• Artificial Intelligence • Previous Articles     Next Articles

Activity Recognition Based on RFID Object Usage for Smart Mobile Devices

Jaeyoung Yang1, Joonwhan Lee2, and Joongmin Choi3   

  1. 1. Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, U.S.A.;
    2. Neowiz Internet Inc., Seoul, Korea;
    3. Department of Computer Science and Engineering, Hanyang University, Ansan, Gyeonggi-Do, 426-791, Korea
  • Received:2010-07-12 Revised:2011-01-31 Online:2011-03-05 Published:2011-03-05
  • About author:Jaeyoung Yang received his B.S., M.S., and Ph.D. degrees in computer science and engineering from Hanyang University, Korea, in 1998, 2000, and 2003 respectively. He is a postdoctoral researcher in Human-Computer Interaction Institute, Carnegie Mellon University, USA. He is also a researcher of Web Intelligent Consortium Korea Center. His research interests include intelligent agents, machine learning, context-awareness, ubiquitous computing, and text mining.
    Joonwhan Lee is the director of Service Develop Division of Neowiz Internet Inc., Seoul, Korea. He received his B.F.A. degree from Seoul National University, Korea, in 1995 and M.Des. degree from Carnegie Mellon University, USA in 2000. And then he received his Ph.D. degree in human-computer interaction from the School of Computer Science in the Carnegie Mellon University in 2008. His research interest focuses on human-computer interaction, interaction design, situationally appropriate user interaction, user interface of pervasive computing, adaptive user interface, and visualization.
    Joongmin Choi is a professor in the Department of Computer Science and Engineering, Hanyang University, Ansan, Korea. He is the director of Intelligent Systems Research Lab. He is also the director of Web Intelligent Consortium (WIC) Korea Center. He received his B.S. and M.S. degrees in computer engineering from Seoul National University, Korea, in 1984 and 1986, respectively, and the Ph.D. degree in computer science from the State University of New York at Buffalo, USA in 1993. His research interest focuses on Web intelligence, which is a somewhat broad concept covering the areas of Web information extraction, Web data mining, semantic Web and ontologies, and other intelligent techniques for manipulating Web information. His other interest areas include intelligent agents, artificial intelligence, and contextaware personalization.
  • Supported by:

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

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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