Context-based similarity measure on human behavior pattern analysis

Methodologies and Application
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Abstract

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.

Keywords

Human behavior pattern Context-based similarity Intelligent environment Process context Lifelog Anomaly detection 

Notes

Acknowledgements

This research was supported by Hankuk University of Foreign Studies Research Fund, and also by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the ministry of Education (2015R1D1A1A01061402)

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human participants

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Industrial and Management EngineeringHankuk University of Foreign StudiesYongin-siSouth Korea

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