AAAI Publications, The Thirty-First International Flairs Conference

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Cognitive Health Prediction on the Elderly Using Sensor Data in Smart Homes
Ramesh Paudel, Kimberlyn Dunn, William Eberle, Danielle Chaung

Last modified: 2018-05-10

Abstract


The percentage of people living over 65 years has increased steadily over the last few decades, and with it is coming a rapid increase in cognitive health issues among the baby boomers. In order to address the issue of caring for this particular aging population, intelligent solutions need to be provided. It is our hypothesis that through the application of various data mining and machine learning approaches, we can analyze data from the sensors installed in smart homes in order to predict whether an elderly resident has cognitive impairments, which will hinder their ability to perform daily tasks. With the growing senior citizen population, it is imperative to detect and try to predict these kinds of behaviors because it can improve the quality and safety of the residents’ home environment as well as provide aid and well-being for their caregiver. In this paper, we present our proposed approach, the real-world data set used in our experiments, and results from this study.

Keywords


Machine Learning, Smart Homes, Congitive Health Prediction

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