Author

Chen Li

Year of Award

6-8-2018

Degree Type

Thesis

Degree Name

Doctor of Philosophy (PhD)

Department

Department of Computer Science.

Principal Supervisor

Cheung, Kwok-wai

Keywords

Bayesian statistical decision theory ; Human activity recognition ; Markov processes

Language

English

Abstract

The elderly living in smart homes can have their daily movement recorded and analyzed. Given the fact that different elders can have their own living habits, a methodology that can automatically identify their daily activities and discover their daily routines will be useful for better elderly care and support. In this thesis research, we focus on developing data mining algorithms for automatic detection of behavioral patterns from the trajectory data of an individual for activity identification, daily routine discovery, and activity prediction. The key challenges for the human activity analysis include the need to consider longer-range dependency of the sensor triggering events for activity modeling and to capture the spatio-temporal variations of the behavioral patterns exhibited by human. We propose to represent the trajectory data using a behavior-aware flow graph which is a probabilistic finite state automaton with its nodes and edges attributed with some local behavior-aware features. Subflows can then be extracted from the flow graph using the kernel k-means as the underlying behavioral patterns for activity identification. Given the identified activities, we propose a novel nominal matrix factorization method under a Bayesian framework with Lasso to extract highly interpretable daily routines. To better take care of the variations of activity durations within each daily routine, we further extend the Bayesian framework with a Markov jump process as the prior to incorporate the shift-invariant property into the model. For empirical evaluation, the proposed methodologies have been compared with a number of existing activity identification and daily routine discovery methods based on both synthetic and publicly available real smart home data sets with promising results obtained. In the thesis, we also illustrate how the proposed unsupervised methodology could be used to support exploratory behavior analysis for elderly care.

Comments

Principal supervisor: Dr. William Kwok-Wai Cheung ; Thesis submitted to the Department of Computer Science ; Thesis (Ph.D.)--Hong Kong Baptist University, 2018

Bibliography

Includes bibliographical references (pages 97-107).



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