Nothing Special   »   [go: up one dir, main page]

Skip to main content

A Mobile Application to Detect Abnormal Patterns of Activity

  • Conference paper
Mobile Computing, Applications, and Services (MobiCASE 2009)

Abstract

In this paper we introduce an unsupervised online clustering algorithm to detect abnormal activities using mobile devices. This algorithm constantly monitors a user’s daily routine and builds his/her personal behavior model through online clustering. When the system observes activities that do not belong to any known normal activities, it immediately generates alert signals so that incidents can be handled in time. In the proposed algorithm, activities are characterized by users’ postures, movements, and their indoor location. Experimental results show that the behavior models are indeed user-specific. Our current system achieves 90% precision and 40% recall for anomalous activity detection.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Projections of the Population by Age and Sex for the United States: 2010 to 2050, http://www.census.gov/population/www/projections/summarytables.html

  2. Western Maine Community Action, Keeping Seniors Home, http://www.wmca.org/Keeping_seniors_home.htm

  3. Falls Among Older Adults: Summary of Research Findings, http://www.cdc.gov/ncipc/pub-res/toolkit/SummaryOfFalls.htm

  4. Bolliger, P.: Redpin - adaptive, zero-configuration indoor localization through user collaboration. In: Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments, pp. 55–60. ACM, New York (2008)

    Chapter  Google Scholar 

  5. Fabian, A., Gyorbiro, N., Homanyi, G.: Activity recognition system for mobile phones using the MotionBand device. In: Proceedings of the 1st International Conference on MOBILe Wireless MiddleWARE, Operating Systems, and Applications, Article No. 41, ICST, Brussels Belgium (2008)

    Google Scholar 

  6. Bao, L., Intille, S.: Activity Recognition from User-Annotated Acceleration Data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  7. Aminian, K., Robert, P., Buchser, E.E., Rutschmann, B., Hayoz, D., Depairon, M.: Physical activity monitoring based on accelerometry: validation and comparison with video observation. Identification of Common Molecular Subsequences 37(3), 304–308 (1999)

    Google Scholar 

  8. Mantyjarvi, J., Himberg, J., Seppanen, T.: Recognizing Human Motion with Multiple Acceleration Sensors. In: 2001 IEEE International Conference on Systems, Man, and Cybernetics, Tucson, AZ, vol. 2, pp. 747–752. IEEE, Los Alamitos (2001)

    Google Scholar 

  9. Randell, C., Muller, H.: Context Awareness by Analysing Accelerometer Data. In: The Fourth International Symposium on Wearable Computers, Atlanta, GA, pp. 175–176. IEEE, Los Alamitos (2000)

    Chapter  Google Scholar 

  10. Krause, A., Sieworik, D., Smailagic, A., Farringdon, J.: Unsupervised, Dynamic Identification of Physiological and Activity Context in Wearable Computing. In: Proceedings of the 7th IEEE International Symposium on Wearable Computers, p. 88. IEEE Computer Society, Washington (2003)

    Google Scholar 

  11. Hein A., Kirste T.: Towards Recognizing Abstract Activities: An Unsupervised Approach. In: Proceedings of the 2nd Workshop on Behaviour Monitoring and Interpretation. pp 102-114. Universitat Bremen, Bremen Germany (2008).

    Google Scholar 

  12. Nguyen, A., Moore, D., McCowan, I.: Unsupervised Clustering of Free-Living Human Activities using Ambulatory Accelerometry. Engineering in Medicine and Biology Society 22, 4895–4898 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering

About this paper

Cite this paper

Baki, O.A., Zhang, J., Griss, M., Lin, T. (2010). A Mobile Application to Detect Abnormal Patterns of Activity. In: Phan, T., Montanari, R., Zerfos, P. (eds) Mobile Computing, Applications, and Services. MobiCASE 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 35. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12607-9_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-12607-9_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12606-2

  • Online ISBN: 978-3-642-12607-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics