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Similarity measure for anomaly detection and comparing human behaviors
Author(s) -
Anderson Derek T.,
Ros María,
Keller James M.,
Cuéllar Manuel P.,
Popescu Mihail,
Delgado Miguel,
Vila Amparo
Publication year - 2012
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21544
Subject(s) - similarity (geometry) , probabilistic logic , anomaly detection , measure (data warehouse) , computer science , similarity measure , context (archaeology) , artificial intelligence , anomaly (physics) , focus (optics) , baseline (sea) , machine learning , cognition , data mining , psychology , physics , biology , geology , optics , image (mathematics) , condensed matter physics , paleontology , oceanography , neuroscience
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.