Activity Recognition Based on RFID Object Usage for Smart Mobile Devices

1. Abe M, Morinishi Y, Maeda A, Aoki M, Inagaki J. A life log collector integrated with a remote-controller for enabling user centric services. IEEE Trans. Consumer Electronics, 2009, 55(2): 295–302.

   Article  Google Scholar 

2. Baek S, Choi E, Huh J, Park K. Sensor information management mechanism for context-aware service in ubiquitous home. IEEE Trans. Consumer Electronics, 2007, 53(4): 1393–1400.

   Article  Google Scholar 

3. Kim J, Jeong D, Baik D. Ontology-based semantic recommendation system in home network environment. IEEE Trans. Consumer Electronics, 2009, 55(3): 1178–1184.

   Article  Google Scholar 

4. Lee H, Lim S, Kim J. UMONS: Ubiquitous monitoring system in smart space. IEEE Trans. Consumer Electronics, 2009, 55(3): 1056–1064.

   Article  MathSciNet  Google Scholar 

5. Patterson D J, Fox D, Kautz H A. Inferring high-level behavior from low-level sensors. In Proc. the 5th Int. Conf. Ubiquitous Computing, Seattle, USA, Oct. 12–15, 2003, pp.73–89.

6. Tapia E M, Intilli S S, Larson K. Activity recognition in the home using simple and ubiquitous sensors. In Proc. Pervasive Computing, Vienna, Austria, April 18–23, 2004, pp.158–175.

7. Duong T V, Bui H H, Phung D Q, Venkatesh S. Activity recognition abnormality detection with the switching hidden semi-Markov model. In Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, San Diego, USA, Jun. 20–26, 2005, pp.838–845.

8. Lao W, Han J, de With P H N. Automative video-based human motion analysis for consumer surveillance system. IEEE Trans. Consumer Electronics, 2009, 55(2): 591–598.

   Article  Google Scholar 

9. Lester J, Choudhury T, Kern N, Borriello G, Hannaford B. A hybrid discriminative/generative approach for modeling human activities. In Proc. Int. Joint Conf. Artificial Intelligence, Edinberg, UK, Jul. 30-Aug. 5, 2005, pp.766–772.

10. Kasteren T V, Noulas A, Englebienne G, Krose B. Accurate activity recognition in a home setting. In Proc. the 10th Int. Conf. Ubiquitous Computing, Tokyo, Japan, Sept. 11–14, 2008, pp.1–8.

11. Patterson D J, Fox D, Kautz H A, Philipose M. Fine-grained activity recognition by aggregating abstract object usage. In Proc. the 9th IEEE Int. Symp. Wearable Computer, Osaka, Japan, Oct. 18–21, 2005, pp.44–51.

12. Lio L, Fox D, Kautz H. Extracting places and activities from GPS traces using hierarchical conditional random fields. Int. J. Robotics Research, 2007, 26(1): 119–134.

    Article  Google Scholar 

13. Landwehr N, Gutmann B, Thon I, Philipose M, Raedt L D. Relational transformation-based tagging for human activity recognition. In Proc. the 6th Int. Workshop on Multi-Relational Data Mining, Warsaw, Poland, Sept. 17, 2007, pp.81–92.

14. Fogarty J, Au C, Hudson S E. Sensing from the basement: A feasibility study of unobtrusive and low-cost home activity recognition. In Proc. the 19th ACM Symp. User Interface Software and Technology, Montreux, Switzerland, Oct. 15–18, 2006, pp.91–100.

15. Modayil J, Bai T, Kautz H. Improving the recognition of interleaved activities. In Proc. the 10th Int. Conf. Ubiquitous Computing, Seoul, Korea, Sept. 21–24, 2008, pp.40–43.

16. Nardi B A. Context and Consciousness: Activity Theory and Human-Computer Interaction. MIT Press, 1996.

17. Rabiner L R. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE, 1989, 77(2): 257–286.

    Article  Google Scholar 

18. Mitchell T. Machine Learning. McGraw Hill, 1997.

19. Sutton C, McCallum A. Introduction to Statistical Relational Learning. MIT Press, 2006, pp.93–128.

20. Wu J, Osuntogun A, Choudhury T, Philipose M, Rehg J M. A scalable approach to activity recognition based on object use. In Proc. the 11th IEEE Int. Conf. Computer Vision, Rio de Janeiro, Brazil, Oct. 14–20, 2007, pp.1–8.

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