The use of hidden semi-markov models in clinical diagnosis maze tasks

Intelligent Data Analysis

Volumn 13, Issue 6, 2009, Pages 943-967

  Marhasev, Einat ^a   Hadad, Meirav ^b   Kaminka, Gal A ^b   Feintuch, Uri ^a,c  

^a UNIVERSITY OF HAIFA   (Israel)

^b BAR ILAN UNIVERSITY   (Israel)

^c HADASSAH HEBREW UNIVERSITY MEDICAL CENTER   (Israel)

Author keywords

Correspondence analysis; Driving; Eye movements; Fuzzy windowing; Hierarchical clustering; Transition matrix; Vigilance

Indexed keywords

EID: 77649133893     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/IDA-2009-0402     Document Type: Article

References (28)

1. H. H. Bui, S. Venkatesh and G. West, Policy recognition in the abstract hidden Markov model. Journal of Artificial Intelligence Research 17 (2002), 451-499.
2. E. Charniak and R. Goldman, A Bayesian model of plan recognition, Artificial Intelligence Journal (64) (1993), 53-79.
3. B.B. Connor, A.M. Wing, G.W. Humphreys, R.M. Bracewell and D.A. Harvey, Errorless learning using haptic guidance: research in cognitive rehabilitation following stroke, In ICDVRAT 2004, pages 77-83, 2002.
4. T.V. Duong, H.H. Bui, D.Q. Phung and S. Venkatesh, Activity recognition and abnormality detection with the switching hidden semi-Markov model, In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR05), 2005.
5. U. Feintuch, J. Haj and P.L. Weiss, Low-cost haptic-aural virtual environments for way-finding by children with congenital blindness: feasibility study. In the 5th IEEE International Workshop on Virtual Rehabilitation, pages 78-81, 2006.
6. U. Feintuch, L. Manevitz, E. Mednikovand, D. Rand, A. Dvorkin, R. Kizony, M. Shahar and P.L. Weiss, Integrating Artificial Intelligence and Virtual Reality in the diagnostic process - Feasibility study. In the 11th Annual Cybertherapy Conference., 2006.
7. U. Feintuch, D. Rand, R. Kizony and P. Weiss, Development of a "low-end" multimodal feedback program for motor, cognitive and sensory rehabilitation, Cyberpsychology & Behavior 7(3) (2004), 281-282.
8. J. Ferguson, Variable duration models for speech. In Symposium on the Application of Hidden Markov Models to Text and Speech, pages 143-179, 1980.
9. D. Fox, J. Hightower, L. Liao, D. Schulz and G. Borriello, Bayesian filtering for location estimation, IEEE Pervasive Computing, 2003.
10. K. Gopalratnam, H.A. Kautz and D.S. Weld, Extending continuous time bayesian networks, In AAAI, pages 981-986, 2005.
11. K. Han and M. Veloso, Automated robot behavior recognition applied to robotic soccer, In IJCAI-99 Workshop on Team Behaviors and Plan Recognition., 1999.
12. S. Hongeng and R. Nevatia, Large-scale event detection using semi-HMMs, In 9th IEEE International Conference on Computer Vision (ICCV'03), 2003.
13. G.A. Kaminka, D.V. Pynadath and M. Tambe, Monitoring teams by overhearing: A multi-agent plan recognition approach, Journal of Artificial Intelligence Research 17 (2002).
14. O. Lahav and D. Mioduser, Multisensory virtual environment for supporting blind persons' acquisition of spatial cognitive mapping, orientation, and mobility skills, In ICDVRAT 2004, pages 213-220, 2002.
15. S. Levinson, Continuously variable duration hidden Markov models for automatic speech recognition, Computation Speech Language (45) (1986), 1-29.
16. E. Marhasev, M. Hadad and G.A. Kaminka, Non-stationary hidden semi markov models in activity recognition, In Proceedings of the AAAI Workshop on Modeling Others from Observations (MOO-06), 2006.
17. P. Natarajan and R. Nevatia, Coupled hidden semi markov models for activity recognition, In IEEE Workshop on Motion and Video Computing, 2007.
18. N. Oliver, E. Horvitz and A. Garg, Layered representation for human activity recognition, In Fourth IEEE International Conference on Multimodal Interfaces (ICMI02), pages 3-8, 2002.
19. S.D. Porteus, Porteus Maze Test, Fifty Years' Application, Pacific Books, Palo Alto, California, 1973.
20. D. Pynadath and M. Wellman, Probabilistic state-dependent grammars for plan recognition. In the Conference on Uncertainty in Artificial Intelligence, pages 507-514, 2000.
21. L.R. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition. In IEEE, volume 2, pages 257-286, 1989.
22. B. Sin and J. Kim, Nonstationary hidden Markov models. Signal Processing 46 (1995), 31-46.
23. M.C. Stevens, R.F. Kaplan and V.M. Hesselbrock, Executive cognitive functioning in the development of antisocial personality disorder, Addictive Behaviors 28 (2003), 285-300.
24. K.A. Sullivan and S.C. Bowden, Which tests do neurophysiologists use? Journal of Clinical Psychology 55 (1997), 657-661.
25. S.V. Vaseghi, State duration modelling in hidden Markov models, Signal Processing 41 (1995), 31-41.
26. J.H.G. Williams, J.T. O'Brien and S. Cullum, The time course of response to electroconvulsive therapy in elderly depressed subjects, International Journal of Geriatric Psychiatry 12 (1997), 563-566.
27. J. Yin, X. Chai and Q. Yang, High-level goal recognition in a wireless LAN, In American Association for Artificial Intelligence, 2004.
28. S.Z. Yu and H. Kobayashi, A hidden semi-Markov model with missing data and multiple observation sequences for mobility tracking, Signal Processing 83(2) (2003), 235-250.