• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2016, Volume: 9, Issue: 24, Pages: 1-8

Original Article

Context-Aware Abnormality Monitoring Service for Care-Needing Persons Using a Probabilistic Model


Background/Objectives: Care-needing persons usually have routine life patterns in their visiting places and routes. Probabilistic models are useful tools to capture the inherent patterns for care-needing persons from a collection of data and to make inference for uncertain situation. Methods/Statistical Analysis: A dynamic Bayesian network model is proposed to capture the traveling patterns for care-needing person. A collection of traveling data having location, date, and battery charge state information is used to train the dynamic Bayesian network model, in which the training travel data are preprocessed to filter out noises using a smoothing filter. The model makes probabilistic inference for the latent random variables on the fly with respect to the arriving captured sensor data. To conduct the probabilistic inference, the Rao-Blackwellized particle filter is used for fast execution. To take protective steps at potentially abnormal situations, the context-based service rules are defined that specify what to do in which situations the care-needing person is. Findings: The situations of interest are evaluated based on the deviation of the inferred probability distribution from the trained ones regarding the latent random variables. The service rules enable to realize value-added services such abnormal state alerting, abnormality notification to stakeholders, and situational information collection at abnormal states for future forensic or tracking. A real data set has been collected from a young student and the proposed method has been trained and tested in an implemented prototype system for young child care. In the experiments for a 6 weeks data set, the method detected all new test routes successfully when the deviation distance is greater than about 20 meters due to the limited precision of the GPS sensors. Improvements/Applications: The proposed model can be applied to context-aware abnormality service for care-needing persons like young children, mentally ill or disabled persons, and elderly persons are exposed to various dangers in daily life.
Keywords: Context-Aware Services, Dynamic Bayesian Network, Graphical Probabilistic Models, Information Services for Minors, Probabilistic Inference


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