Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Training-Free Fuzzy Logic Based Human Activity Recognition

  • Kim, Eunju (Mobile and Pervasive Computing Lab., Department of Computer and Information Science and Engineering, University of Florida) ;
  • Helal, Sumi (Mobile and Pervasive Computing Lab., Department of Computer and Information Science and Engineering, University of Florida)
  • Received : 2014.07.09
  • Accepted : 2014.08.01
  • Published : 2014.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

The accuracy of training-based activity recognition depends on the training procedure and the extent to which the training dataset comprehensively represents the activity and its varieties. Additionally, training incurs substantial cost and effort in the process of collecting training data. To address these limitations, we have developed a training-free activity recognition approach based on a fuzzy logic algorithm that utilizes a generic activity model and an associated activity semantic knowledge. The approach is validated through experimentation with real activity datasets. Results show that the fuzzy logic based algorithms exhibit comparable or better accuracy than other training-based approaches.

Keywords

References

  1. A. A. Helal, M. Mokhtari, and B. Abdulrazak, The Engineering Handbook of Smart Technology for Aging, Disability, and Independence. Hoboken, NJ: Wiley, 2008, pp. 1-26.
  2. S. Helal and E. Kim, "A practical activity recognition approach based on the generic activity framework," International Journal of E-Health and Medical Communications, vol. 3, no. 3, pp. 54-71, Jul. 2012. https://doi.org/10.4018/jehmc.2012070105
  3. L. Constantine, "Human activity modeling: toward a pragmatic integration of activity theory and usage-centered design," in Human-Centered Software Engineering, A. Seffah, J. Vanderdonckt, and M. Desmarais, editors. London: Springer, 2009, pp. 27-51.
  4. T. van Kasteren, A. Noulas, G. Englebienne, and B. Krose, "Accurate activity recognition in a home setting," in Proceedings of the 10th International Conference on Ubiquitous Computing, Seoul, Korea, September 21-24, 2008.
  5. E. Kim, S. Helal, and D. Cook, "Human activity recognition and pattern discovery," IEEE Pervasive Computing, vol. 9, no. 1, pp. 48-53, Jan. 2010.
  6. V. V. Davydov, V. P. Zinchenko, and N. F. Talyzina, "The problem of activity in the works of A. N. Leontjev," Soviet Psychology, vol. 21, no. 4, pp. 31-42, 1983.
  7. K. Kuutti, "Activity theory as a potential framework for human-computer interaction research," in Context and Consciousness: Activity Theory and Human-Computer Interaction, B. A. Nardi, Ed., Cambridge, MA: MIT Press, 1996, pp. 17-44.
  8. B. A. Nardi, "Studying context: a comparison of activity theory, situated action models, and distributed cognition," in Context and Consciousness: Activity Theory and Human-Computer Interaction, B. A. Nardi, Ed., Cambridge, MA: MIT Press, 1996, pp. 69-102.
  9. L. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition," Proceedings of the IEEE, vol. 77, no. 2, pp. 257-286, Feb. 1989. https://doi.org/10.1109/5.18626
  10. C. Sutton and A. McCallum, "An introduction to conditional random fields for relational learning," in Introduction to Statistical Relational Learning, L. Getoor and B. Taskar, Eds., Cambridge, MA: MIT Press, 2007, pp. 93-128.
  11. H. M. Wallach, "Conditional random fields: an introduction," University of Pennsylvania CIS, Philadelphia, PA, Technical Report MS-CIS-04-21, 2004.
  12. V. Novak, I. Perfilieva, and J. Mockor, Mathematical Principles of Fuzzy Logic. Boston, MA: Kluwer Academic, 1999.
  13. G. Chen and T. T. Pham, Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems. Boca Raton, FL: CRC Press, 2001.
  14. A. Abraham, "Rule-based expert systems," in Handbook of Measuring System Design, P. H. Sydenham and R. Thorn, Eds., Chichester, England: Wiley, 2005, pp. 909-919.
  15. S. A. Alvarez, "An exact analytical relation among recall, precision, and classification accuracy in information retrieval," Computer Science Department, Boston College, Chestnut Hill, MA, Technical Report BCCS-02-01, 2002.
  16. P. Lingras and C. J. Butz, "Precision and recall in rough support vector machines," in Proceedings of the IEEE International Conference on Granular Computing, Fremont, CA, November 2-4, 2007, pp. 654-654.
  17. W. Pentney and M. Philipose, "Sensor-based understanding of daily life via large-scale use of common sense," In Proceedings of the 21st National Conference on Artificial Intelligence, Boston, MA, 2006.
  18. D. Surie, T. Pederson, F. Lagriffoul, L. E. Janlert, and D. Sjolie, "Activity recognition using an egocentric perspective of everyday objects," in Ubiquitous Intelligence and Computing, Lecture Notes in Computer Science Volume 4611, J. Indulska, J. Ma, L. Yang, T. Ungerer, and J. Cao, Eds., Heidelberg: Springer Berlin, 2007, pp. 246-257.
  19. A. Helal, D. J. Cook, and M. Schmalz, "Smart home-based health platform for behavioral monitoring and alteration of diabetes patients," Journal of Diabetes Science and Technology, vol. 3, no. 1, pp. 141-148, Jan. 2009. https://doi.org/10.1177/193229680900300115
  20. S. Helal, C. Chao, K. Eunju, R. Bose, and L. Choonhwa, "Toward an ecosystem for developing and programming assistive environments," Proceedings of the IEEE, vol. 100, no. 8, pp. 2489-2504, Aug. 2012. https://doi.org/10.1109/JPROC.2012.2200548
  21. The Gator Tech Smart House: an experimental facility at the University of Florida [Online]. Available: http://www.icta.ufl.edu/gt.htm.
  22. Ceyon Technology [Online]. Available: http://www.ceyon.co.kr/.
  23. Phidgets [Online]. Available: http://www.phidgets.com/.

Cited by

  1. Study on the performance evaluation of online teaching using the quantile regression analysis and artificial neural network vol.72, pp.3, 2016, https://doi.org/10.1007/s11227-015-1599-1
  2. Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement vol.16, pp.12, 2016, https://doi.org/10.3390/s16122053
  3. An adaptive hidden Markov model-based gesture recognition approach using Kinect to simplify large-scale video data processing for humanoid robot imitation vol.75, pp.23, 2016, https://doi.org/10.1007/s11042-015-2505-9