Context-based similarity measure on human behavior pattern analysis

Similarity measures for analyzing human behavior patterns are inseparable part of the intelligent environment, with the assistive functionality as its core value. The measure must represent the contexts properly which characterizes the users’ environment. Recent studies attempted to formulate simila...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2019-07, Vol.23 (14), p.5455-5467
Main Authors: Prabono, Aria Ghora, Lee, Seok-Lyong, Yahya, Bernardo Nugroho
Format: Article
Language:English
Subjects:
Anomalies
Artificial Intelligence
Computational Intelligence
Context
Control
Engineering
Human behavior
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Pattern analysis
Recommender systems
Robotics
Semantics
Similarity
Similarity measures
User behavior
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Summary:Similarity measures for analyzing human behavior patterns are inseparable part of the intelligent environment, with the assistive functionality as its core value. The measure must represent the contexts properly which characterizes the users’ environment. Recent studies attempted to formulate similarity measures for the intelligent environment by incorporating relevant contexts. Yet, they are lacking the integration of multiple inter-related important contexts, which leads to model underestimation and possibly the wrong interpretation. This work proposes a context-based similarity measure for analyzing human behavior patterns. The proposed similarity measure extends and combines the commonly used contexts (i.e., activity, location, and time) into a holistic measure. To avoid the biased representation of activity context similarity, we add one more aspect, namely process context , which describes a wide range of interval relations among the activities of a user. The proposed approach is compared with state-of-the art similarity measures by evaluating both real and simulated data. The result shows that our approach yields the better result in terms of robustness toward noises. In addition, our approach also shows a better reliability compared to previous works in the case of anomaly detection.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-018-3198-6