A Mobile Application to Detect Abnormal Patterns of Activity

Omar Baki; Joy Zhang; Martin Griss; Tony Lin

Mobile Computing, Applications, and Services. First International ICST Conference, MobiCASE 2009, San Diego, CA, USA, October 26-29, 2009, Revised Selected Papers

Research Article

A Mobile Application to Detect Abnormal Patterns of Activity

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@INPROCEEDINGS{10.1007/978-3-642-12607-9_13,
    author={Omar Baki and Joy Zhang and Martin Griss and Tony Lin},
    title={A Mobile Application to Detect Abnormal Patterns of Activity},
    proceedings={Mobile Computing, Applications, and Services. First International ICST Conference, MobiCASE 2009, San Diego, CA, USA, October 26-29, 2009, Revised Selected Papers},
    proceedings_a={MOBICASE},
    year={2012},
    month={10},
    keywords={activity monitoring context-aware abnormality detection unsupervised learning online clustering senior care},
    doi={10.1007/978-3-642-12607-9_13}
}

Omar Baki
Joy Zhang
Martin Griss
Tony Lin
Year: 2012
A Mobile Application to Detect Abnormal Patterns of Activity
MOBICASE
Springer
DOI: 10.1007/978-3-642-12607-9_13

Omar Baki¹^,*, Joy Zhang¹^,*, Martin Griss¹^,*, Tony Lin¹^,*

1: Carnegie Mellon Silicon Valley

*Contact email: omar.abdulbaki@sv.cmu.edu, joy.zhang@sv.cmu.edu, martin.griss@sv.cmu.edu, tony.lin@sv.cmu.edu

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.

Keywords: activity monitoring context-aware abnormality detection unsupervised learning online clustering senior care

Published: 2012-10-26

: http://dx.doi.org/10.1007/978-3-642-12607-9_13