Using dynamic time warping for online temporal fusion in multisensor systems

Information Fusion

Volumn 9, Issue 3, 2008, Pages 370-388

  Ko, Ming Hsiao ^a   West, Geoff ^a   Venkatesh, Svetha ^a   Kumar, Mohan ^b  

^a CURTIN UNIVERSITY   (Australia)

^b The University of Texas at Arlington   (United States)

Author keywords

Context awareness; Dynamic time warping; Gesture recognition; Temporal fusion

Indexed keywords

DECISION THEORY; ENVIRONMENTAL IMPACT; GESTURE RECOGNITION; HIDDEN MARKOV MODELS; ONLINE SYSTEMS; TEMPORAL LOGIC; TIME VARYING NETWORKS;

CONTEXT AWARENESS; DYNAMIC TIME WARPING; HMM SYSTEMS; ONLINE TEMPORAL FUSION;

SENSOR DATA FUSION;

EID: 43549120257     PISSN: 15662535     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.inffus.2006.08.002     Document Type: Article

References (48)

1. Dasarathy B.V. Decision Fusion (1994), IEEE Computer Society Press, Washington, DC
2. Aziz A.M., Tummala M., and Cristi R. Fuzzy logic data correlation approach in multisensor-multitarget tracking systems. Signal Processing 76 2 (1999) 195-209
3. Jouseau E., and Dorizzi B. Neural network and fuzzy data fusion: application to an on-line and real-time vehicle detection system. Pattern Recognition Letters 20 (1999) 97-107
4. F. Opitz, W. Henrich, T. Kausch, Data fusion development concepts within complex surveillance systems, in: The 7th International Conference on Information Fusion, Stockholm, Sweden, 2004.
5. Morriss S.B. Automated Manufacturing Systems - Actuators, Controls, Sensors and Robotics (1994), McGraw-Hill
6. Castellanos J.A., and Tards J.D. Mobile Robot Localization and Map Building: A Multisensor Fusion Approach (1999), Kluwer Academic Publishers, Boston
7. Venkatasubramaniam V. A review of process fault detection and diagnosis: Part I: Quantitative model-based methods. Computers and Chemical Engineering 27 15 (2003) 293-311
8. R. DeVaul, M. Sung, J. Gips, A. Pentland, Mithril 2003: applications and architecture, in: 7th IEEE International Symposium on Wearable Computers, New York, 2003.
9. J. Mantyjarvi, Sensor-based context recognition for mobile applications, Ph.D. thesis, VTT Publications, 2003.
10. Lee J.-M., Yoo C., and Lee I.-B. On-line batch process monitoring using a consecutively updated multiway principal component analysis model. Computers & Chemical Engineering 27 12 (2003) 1903-1912
11. G. Chambers, S. Venkatesh, G. West, H. Bui, Segmentation of intentional gestures for sports video annotation, in: 10th IEEE International Multimedia Modelling Conference, Brisbane, Australia, 2004, pp. 124-129.
12. A. Krause, D. Siewiorek, A. Smailagic, J. Farringdon, Unsupervised, dynamic identification of physiological and activity context in wearable computing, in: 7th IEEE International Symposium on Wearable Computers, New York, 2003.
13. J. Mantyjarvi, J. Himberg, P. Kangas, U. Tuomela, P. Huuskonen, Sensor signal data set for exploring context recognition of mobile devices, in: Workshop "Benchmarks and a database for context recognition" of 2nd International conference on Pervasive Computing (PERVASIVE 2004), Linz/Vienna, Austria, 2004.
14. Sakoe H., and Chiba S. Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing 26 (1978) 43-49
15. Bellman R. Dynamic Programming (1957), Princeton University Press, Princeton, NJ
16. Myers C.S., Rabiner L.R., and Rosenberg A.E. Performance tradeoffs in dynamic time warping algorithms for isolated word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing ASSP-28 6 (1980) 623-635
17. Rabiner L.R., Rosenberg A.E., and Levinson S.E. Considerations in dynamic time warping algorithms for discrete word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing ASSP-26 4 (1978) 575-582
18. Myers C.S., and Rabiner L.R. A level building dynamic time warping algorithm for connected word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing ASSP-29 2 (1981) 284-297
19. Rabiner L.R., and Myers C.S. Connected digit recognition using a level building DTW algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing ASSP-29 3 (1981) 351-363
20. C.S. Myers, L.R. Rabiner, A.E. Rosenberg, An investigation of the use of dynamic time warping for word spotting and connected speech recognition, in: IEEE International Conference on Acoustics, Speech, and Signal Processing, 1980, pp. 173-177.
21. E. Keogh, A fast and robust method for pattern matching in time series databases, in: Proceedings of 9th International Conference on Tools with Artificial Intelligence (TAI'97), Newport Beach, CA, 1997, pp. 578-584.
22. Keogh E., and Ratanamahatana C.A. Exact indexing of dynamic time warping. Knowledge and Information Systems: An International Journal (KAIS) (2004) 358-386
23. J. Lin, M. Vlachos, E. Keogh, D. Gunopulos, Iterative incremental clustering of time series, in: Proceedings of the IX Conference on Extending Database Technology, Crete, Greece, 2004.
24. M. Hossain, M. Jenkin, Recognizing hand-raising gestures using HMM, in: Second Canadian Conference on Computer and Robot Vision (CRV 2005), 2005, pp. 405-412.
25. S. Kallio, J. Kela, J. Mantyjarvi, Online gesture recognition system for mobile interaction, in: IEEE International Conference on Systems, Man and Cybernetics, Washington, DC, USA, 2003.
26. A. Wilson, A. Bobick, Realtime online adaptive gesture recognition, in: Proceedings of the International Conference on Pattern Recognition, Barcelona, Spain, 2000.
27. Rabiner L.R. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of IEEE 77 2 (1989) 257-286
28. C.A. Ratanamahatana, E. Keogh, Three myths about dynamic time warping, in: SIAM 2005 Data Mining Conference, CA, 2005.
29. M.H. Ko, G. West, S. Venkatesh, M. Kumar, Temporal data fusion in multisensor systems using dynamic time warping, in: Workshop on Information Fusion and Dissemination in Wireless Sensor Networks (SENSORFUSION 2005) co-located with WICON 2005, Budapest, Hungary, 2005.
30. Crochiere R.E., Tribolet J.M., and Rabiner L.R. An improved endpoint detector for isolated word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing COM-29 5 (1981) 621-659
31. Wilpon J.G., and Rabiner L.R. Application of hidden Markov models to automatic speech endpoint detection. Computer Speech and Language 2 3/4 (1987) 947-954
32. M.H. Ko, G. West, S. Venkatesh, M. Kumar, Online context recognition in multisensor systems using dynamic time warping, in: Proceedings of the Second International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2005), Melbourne, Australia, 2005.
33. C.A. Ratanamahatana, E.J. Keogh, Everything you know about dynamic time warping is wrong, in: Third Workshop on Mining Temporal and Sequential Data, in conjunction with KDD-2004, Seattle, WA, 2004.
34. Itakura F. Minimum prediction residual principle applied to speech recognition. IEEE Transactions on Acoustics Speech Signal Process ASSP-23 (1975) 52-72
35. Rabiner L.R., and Juang B.H. Fundamentals of Speech Recognition (1993), Prentice Hall, Englewood Cliffs, NJ
36. Kuhn M.H., and Tomaschewski H.H. Improvements in isolated word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing ASSP-31 1 (1983) 157-167
37. C.A. Ratanamahatana, E.J. Keogh, Making time-series classification more accurate using learned constraints, in: Proceedings of the Fourth SIAM International Conference on Data Mining, Lake Buena Vista, FL, USA, 2004.
38. A. Schmidt, Ubiquitous computing - computing in context, Ph.D. thesis, Lancaster University, 2002.
39. W.H. Abdulla, D. Chow, G. Sin, Cross-words reference template for DTW based speech recognition systems, in: IEEE TENCON 2003, Bangalore, India, 2003.
40. Murphy K. The Bayes net toolbox for Matlab. Computing Science and Statistics 33 (2001) 257-286
41. Analog Devices Inc., ADXL202 dual axis accelerometer, http://www.analog.com/en/prod/0,2877,ADXL202,00.html (2005).
42. D. Shepherd, BBC sport academy cricket umpire signals, http://news.bbc.co.uk/sportacademy/bsp/hi/cricket/rules/umpires_signals/htm.
43. Rabiner L.R. Some properties of continuous hidden Markov model representations. AT&T Technical Journal 64 6 (1985) 1251-1270
44. Xue H., and Govindaraju V. Hidden Markov models combining discrete symbols and continuous attributes in handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 3 (2006) 458-462
45. J.A. Flanagan, J. Himberg, J. Mantyjarvi, A hierarchical approach to learning context and facilitating user interaction in mobile devices, in: Artificial Intelligence in Mobile System 2003 (AIMS 2003 in conjunction with Ubicomp 2003), Seattle, USA, 2003.
46. J.A. Flanagan, J. Mantyjarvi, J. Himberg, Unsupervised clustering of symbol strings and context recognition, in: Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002), Maebashi, Japan, 2002.
47. J. Himberg, J.A. Flanagan, J. Mantyjarvi, Towards context awareness using symbol clustering map, in: Proceedings of the Workshop for Self-Organizing Maps 2003 (WSOM2003), Kitakyushu, Japan, 2003.
48. Kohonen T. Self-Organizating Maps. second ed. (2000), Springer-Verlag