Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Elderly mobility and daily routine analysis based on behavior-aware flow graph modeling

Language

English

Abstract

With the advent of ubiquitous computing and sensor technologies, indoor trajectory data of individuals can readily be collected to support analysis of their underlying movement behaviors. Different methods for activity recognition have been proposed where supervised learning algorithms are often adopted. In many applications like elderly care, the behaviors to be characterized are often not known in a priori and the behaviors of different individuals cannot be assumed similar even for the same activity type. In this paper, we propose an unsupervised learning methodology to first extract from the trajectory data of an individual behavioral patterns as representations of his/her daily activities, and then infer the patterns' occurrence per day for daily routine analysis. Extracting behavioral patterns is challenging as the patterns often carry long-range dependency and appear with spatio-temporal variations, making the conventional frequent pattern mining approach not suitable. We propose to model the trajectory data using a behavior-aware flow graph which is a probabilistic finite state automaton where the nodes and edges are attributed with local behavioral features. We then apply the weighted kernel k-means algorithm to the flow graph to identify sub flows as the behavioral patterns, followed by non-negative matrix factorization to compute the daily routine patterns. To evaluate the effectiveness of the proposed approach, we applied it to a publicly available data set that contains trajectories of an elder living in a smart home for 219 days. With reference to the ground truth, our experimental results show that the proposed flow graph allows more accurate activity-specific behavioral patterns to be extracted as compared to a frequent pattern clustering approach. Also, we illustrate how the proposed method can be used to support daily routine analysis.

Keywords

Trajectory, Data mining, Probabilistic logic, Context, Automata, Senior citizens, Data models, smart house, probabilistic grammar model, graph-based clustering, daily routine mining

Publication Date

10-2015

Source Publication Title

2015 International Conference on Healthcare Informatics

Start Page

427

End Page

436

Conference Location

Dallas, United States

Publisher

The Institute of Electrical and Electronics Engineers, Inc.

Copyright

Copyright © 2015 by The Institute of Electrical and Electronics Engineers, Inc.

Funder

This work was partially supported by Hong Kong Baptist University Strategic Development Fund.

DOI

10.1109/ICHI.2015.60

Link to Publisher's Edition

http://dx.doi.org/10.1109/ICHI.2015.60

ISBN (print)

9781467395472

ISBN (electronic)

9781467395489

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