Internet of things and machine learning for healthy ageing: identifying the early signs of dementia
Files
(Published version)
Date
2020
Authors
Ahamed, F.
Shahrestani, S.
Cheung, H.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
Sensors, 2020; 20(21):1-25
Statement of Responsibility
Conference Name
Abstract
Identifying the symptoms of the early stages of dementia is a difficult task, particularly forolder adults living in residential care. Internet of Things (IoT) and smart environments can assistwith the early detection of dementia, by nonintrusive monitoring of the daily activities of the olderadults. In this work, we focus on the daily life activities of adults in a smart home setting to discovertheir potential cognitive anomalies using a public dataset. After analysing the dataset, extracting thefeatures, and selecting distinctive features based on dynamic ranking, a classification model is built.We compare and contrast several machine learning approaches for developing a reliable and efficientmodel to identify the cognitive status of monitored adults. Using our predictive model and ourapproach of distinctive feature selection, we have achieved 90.74% accuracy in detecting the onsetof dementia.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)