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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Projections of the Population by Age and Sex for the United States: 2010 to 2050, http://www.census.gov/population/www/projections/summarytables.html
Western Maine Community Action, Keeping Seniors Home, http://www.wmca.org/Keeping_seniors_home.htm
Falls Among Older Adults: Summary of Research Findings, http://www.cdc.gov/ncipc/pub-res/toolkit/SummaryOfFalls.htm
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)
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)
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)
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)
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)
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)
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)
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).
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights 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)