Similarity measure for anomaly detection and comparing human behaviors

  • Authors:

  • Derek T. Anderson;María Ros;James M. Keller;Manuel P. Cuéllar;Mihail Popescu;Miguel Delgado;Amparo Vila

  • Affiliations:

  • Electrical and Computer Engineering Department, Mississippi State University, Mississippi State, MS 39762;Ciencias de la Computación e Inteligencia Artificial, Universidad de Granada, Granada, Spain;Electrical Engineering Department, University of Missouri, Columbia, MO 65211;Ciencias de la Computación e Inteligencia Artificial, Universidad de Granada, Granada, Spain;Health Management and Informatics Department, University of Missouri, Columbia, MO 65212;Ciencias de la Computación e Inteligencia Artificial, Universidad de Granada, Granada, Spain;Ciencias de la Computación e Inteligencia Artificial, Universidad de Granada, Granada, Spain

  • Venue:
  • International Journal of Intelligent Systems
  • Year:
  • 2012

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Abstract

Herein, we put forth a new similarity measure for anomaly detection and for comparing human behaviors based on the theories of learning automata, comparison of soft partitions, and temporal probabilistic order relations. In particular, focus is placed on monitoring individuals in a home setting for their own well-being. This work is a high-level investigation focused on the structure of human behavior. Examples demonstrate the utility of this approach for (1) understanding the similarity of pairs of behaviors for an individual (or alternatively between individuals) and (2) detecting significant change between changing behavior and a baseline model. In the context of eldercare, significant change in behavior can be a precursor to cognitive and/or functional health related problems. Simulated resident behavior is used to show different scenarios and the response of the proposed measure. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.