Intelligent Health Support System for Elderly via Behavior Models in IoT Environment
Date Issued
2016
Date
2016
Author(s)
Tsai, Ming-Je
Abstract
Due to longer life expectancy and declining fertility rates, ageing society is arriving. Nowadays, close monitoring of daily activities of elders is enabled by employment of the advanced wireless sensor networks and Internet of Things, whose large quantity da-ta are then analyzed by activity recognition techniques whereby their behavior can be accurately modelled. In general, behavior is important information about how elders live, and caregivers can thus take care of elders more easily with the help from that infor-mation. So far, there are many research results related to learning of human behavior; however, their assumptions are usually either too simple or inflexible to account for complex human behavior in real life, which change dynamically depending on daily life pattern. We here present a daily pattern based framework for human behavior learning and prediction. Such framework discovers contexts like start time and duration of activ-ity from resident’s real life data and relations between activities, respectively. Learning multiple behavior models can achieve the accurate activity prediction and the prelimi-nary anomaly detection. Elders can examine the lifestyle of themselves and caregivers also can obtain helpful information. When anomaly happens, the system notifies elders and caregivers immediately. We evaluate the prediction accuracy on two public datasets and our own datasets, and also the anomaly detection rate on our own data, and the ex-perimental results have shown promising results.
Subjects
Behavior Modelling
Activity Prediction
Adaptive Learning Model
Intelligent Health Support System
Internet of Things
SDGs
Type
thesis
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