Improving activity discovery with automatic neighborhood estimation

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2007, Pages 2814-2819

  Minnen, David ^a   Starner, Thad ^a   Essa, Irfan ^a   Isbell, Charles ^a  

^a Georgia Institute of Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVITY DISCOVERIES; MOTIF DISCOVERY; MULTIDIMENSIONAL SENSOR DATUM; SENSORY SIGNALS;

ALGORITHMS;

ARTIFICIAL INTELLIGENCE;

EID: 84880902891     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. T.L. Bailey and C. Elkan. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. In Second International Conference on Intelligent Systems for Molecular Biology, pages 28-36. AAAI Press, 1994.
2. Ling Bao and Stephen S. Intille. Activity recognition from user-annotated acceleration data. In A. Ferscha and F. Mattern, editors, Pervasive, volume 3001 of Lecture Notes in Computer Science, pages 1-17. Springer, 2004.
3. K. Blekas, D. Fotiadis, and A. Likas. Greedy mixture learning for multiple motif discovery in biological sequences. Biinformatics, 19(5), 2003.
4. B. Chiu, E. Keogh, and S. Lonardi. Probabilistic disovery of time series motifs. In Conf. on Knowledge Discovery in Data, pages 493-498, 2003.
5. Paul R. Cohen, J. Tim Oates, and Carole R. Beal. Robots that learn meanings. In submitted to First Joint Conference on Autonomous Agents and Multiagent Systems, 2002.
6. T. Cormen, C. Leiserson, and R. Rivest. Introduction to Algorithms. The MIT Press, 1997.
7. K.L. Jensen, M. P. Styczynski, Isidore Rigoutsos, and G.N. Stephanopoulos. A generic motif discovery algorithm for sequential data. Bioinformatics, 22(1):21-28, 2006. (Pubitemid 43057004)
8. E. Keogh and T. Folias. UCR time series data mining archive, 2002.
9. E. Keogh, K. Chakrabarti, M.J. Pazzani, and S. Mehrotra. Dimensionality reduction for fast similarity search in large time series databases. Journal of Knowledge and Information Systems, 3(3):263-286, 2000.
10. E. Keogh, S. Lonardi, and C. Ratanamahatana. Towards parameter-free data mining. In ACMSIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2004.
11. E. Keogh. Personal communication, 2006.
12. J. Lin, E. Keogh, S. Lonardi, and P. Patel. Finding motifs in time series. In Proceedings of the Second Workshop on Temporal Data Mining, Edmonton, Alberta, Canada, July 2002.
13. J. Lin, E. Keogh, S. Lonardi, and B. Chiu. A symbolic representation of time series, with implications for streaming algorithms. In 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, June 2003.
14. D. Minnen, I. Essa, and T. Starner. Expectation grammars: Leveraging high-level expectations for activity recognition. In CVPR 2003, June 2003.
15. D. Minnen, T. Starner, I. Essa, and C. Isbell. Discovering characteristic actions from on-body sensor data. In International Symposium on Wearable Computers, pages 11-18, October 2006.
16. D. Minnen, T. Westeyn, T. Starner, J.A. Ward, and P. Lukowicz. Performance metrics and evaluation issues for continuous activity recognition. In Performance Metrics for Intelligent Systems, 2006.
17. T. Oates. PERUSE: An unsupervised algorithm for finding recurring patterns in time series. In Int. Conf. on Data Mining, pages 330-337, 2002.
18. V. Pavlovic, J.M. Rehg, and J. MacCormick. Learning switching linear models of human motion. In NIPS, pages 981-987, 2000.
19. C.A. Ratanamahatana and E. Keogh. Three myths about dynamic time warping. In SIAM Int. Conf. on Data Mining, pages 506-510, 2005.
20. T. Starner, J. Weaver, and A.P. Pentland. Real-time american sign language recognition using desk and wearable computer based video. PAMI, 20(12):1371-1375, Dec. 1998. (Pubitemid 128741382)
21. Yoshiki Tanaka, Kazuhisa Iwamoto, and Kuniaki Uehara. Discovery of time-series motif from multi-dimensional data based on mdl principle. Machine Learning, 58(2-3):269-300, 2005.