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.
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© 2010 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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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
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DOI: https://doi.org/10.1007/978-3-642-12607-9_13
Publisher Name: Springer, Berlin, Heidelberg
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