skip to main content
10.1145/1409635.1409638acmotherconferencesArticle/Chapter ViewAbstractPublication PagesubicompConference Proceedingsconference-collections
research-article

Discovery of activity patterns using topic models

Published:21 September 2008Publication History

ABSTRACT

In this work we propose a novel method to recognize daily routines as a probabilistic combination of activity patterns. The use of topic models enables the automatic discovery of such patterns in a user's daily routine. We report experimental results that show the ability of the approach to model and recognize daily routines without user annotation.

References

  1. O. Amft, C. Lombriser, T. Stiefmeier, and G. Tröster. Recognition of user activity sequences using distributed event detection. In Second European Conference on Smart Sensing and Context (EuroSSC), October 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. J. Begole, J. Tang, and R. Hill. Rhythm Modeling, Visualizations, and Applications. Proceedings of the ACM Symposium on User Interface Software and Technology (UIST 2003), pages 11--20, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. D. Blei. C implementation of variational EM for latent Dirichlet allocation (LDA), available at http://www.cs.princeton.edu/blei/lda-c/, 2006.Google ScholarGoogle Scholar
  4. D. Blei, A. Ng, and M. Jordan. Latent dirichlet allocation. The Journal of Machine Learning Research, 3:993--1022, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. B. Clarkson and A. Pentland. Unsupervised clustering of ambulatory audio and video. In icassp, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. N. Eagle and A. Pentland. Reality mining: sensing complex social systems. Personal and Ubiquitous Computing, 10(4):255--268, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. R. Hamid, S. Maddi, A. Johnson, A. Bobick, and C. I. I. Essa. Unsupervised discovery and characterization of activities from event-streams. In UAI, 2005.Google ScholarGoogle Scholar
  8. T. Hofmann. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning Journal, 42(1):177--197, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. E. Horvitz, P. Koch, C. M. Kadie, and A. Jacobs. Coordinate: Probabilistic Forecasting of Presence and Availability. In Proc. UAI, pages 224--233. Morgan Kaufmann Publishers, July 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. T. Huynh, U. Blanke, and B. Schiele. Scalable recognition of daily activities with wearable sensors. In 3rd International Symposium on Location- and Context-Awareness (LoCA), pages 50--67, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. J. Krumm and E. Horvitz. Predestination: Inferring Destinations from Partial Trajectories. In Proc. UbiComp, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. L. Liao, D. Fox, and H. Kautz. Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields. The International Journal of Robotics Research, 26(1):119, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. D. Minnen, T. Starner, I. Essa, and C. Isbell. Discovering characteristic actions from on-body sensor data. In Proc. ISWC, October 2006.Google ScholarGoogle ScholarCross RefCross Ref
  14. D. Minnen, T. Starner, J. Ward, P. Lukowicz, and G. Troster. Recognizing and Discovering Human Actions from On-Body Sensor Data. In Proc. ICME, pages 1545--1548, 2005.Google ScholarGoogle ScholarCross RefCross Ref
  15. U. Naeem, J. Bigham, and J. Wang. Recognising Activities of Daily Life using Hierarchical Plans. In EuroSSC, October 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. N. Oliver, E. Horvitz, and A. Garg. Layered representations for human activity recognition. Proc. ICMI, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. D. Patterson, D. Fox, H. Kautz, and M. Philipose. Fine-grained activity recognition by aggregating abstract object usage. In Proc. ISWC, pages 44--51, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. M. Philipose, K. P. Fishkin, M. Perkowitz, D. J. P. D. Hahnel, D. Fox, and H. Kautz. Inferring Activities from Interactions with Objects. IEEE Pervasive Computing: Mobile and Ubiquitous Systems, 3(4):50--57, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. K. Van Laerhoven, H. Gellersen, and Y. Malliaris. Long-Term Activity Monitoring with a Wearable Sensor Node. BSN Workshop, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. J. Zacks and B. Tversky. Event structure in perception and conception. Psychological Bulletin, 127(1), 2001.Google ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Other conferences
    UbiComp '08: Proceedings of the 10th international conference on Ubiquitous computing
    September 2008
    404 pages
    ISBN:9781605581361
    DOI:10.1145/1409635

    Copyright © 2008 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 21 September 2008

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article

    Acceptance Rates

    Overall Acceptance Rate764of2,912submissions,26%

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader