Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia

Identifying the symptoms of the early stages of dementia is a difficult task, particularly for older adults living in residential care. Internet of Things (IoT) and smart environments can assist with the early detection of dementia, by nonintrusive monitoring of the daily activities of the older adu...

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Main Authors: Farhad Ahamed, Seyed Shahrestani, Hon Cheung
Format: Article
Language:English
Published: MDPI AG 2020-10-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/20/21/6031
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spelling doaj-6b72dd7f10074f85929f92be9e2583562020-11-25T03:42:54ZengMDPI AGSensors1424-82202020-10-01206031603110.3390/s20216031Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of DementiaFarhad Ahamed0Seyed Shahrestani1Hon Cheung2School of Computer, Data and Mathematical Sciences, Western Sydney University, Second Ave, Kingswood, NSW 2747, AustraliaSchool of Computer, Data and Mathematical Sciences, Western Sydney University, Second Ave, Kingswood, NSW 2747, AustraliaSchool of Computer, Data and Mathematical Sciences, Western Sydney University, Second Ave, Kingswood, NSW 2747, AustraliaIdentifying the symptoms of the early stages of dementia is a difficult task, particularly for older adults living in residential care. Internet of Things (IoT) and smart environments can assist with the early detection of dementia, by nonintrusive monitoring of the daily activities of the older adults. In this work, we focus on the daily life activities of adults in a smart home setting to discover their potential cognitive anomalies using a public dataset. After analysing the dataset, extracting the features, 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 efficient model to identify the cognitive status of monitored adults. Using our predictive model and our approach of distinctive feature selection, we have achieved 90.74% accuracy in detecting the onset of dementia.https://www.mdpi.com/1424-8220/20/21/6031dementiainternet of thingsmachine learningIoT in healthcareIoT in dementia caredementia and smart environment
collection DOAJ
language English
format Article
sources DOAJ
author Farhad Ahamed
Seyed Shahrestani
Hon Cheung
spellingShingle Farhad Ahamed
Seyed Shahrestani
Hon Cheung
Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia
Sensors
dementia
internet of things
machine learning
IoT in healthcare
IoT in dementia care
dementia and smart environment
author_facet Farhad Ahamed
Seyed Shahrestani
Hon Cheung
author_sort Farhad Ahamed
title Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia
title_short Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia
title_full Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia
title_fullStr Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia
title_full_unstemmed Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia
title_sort internet of things and machine learning for healthy ageing: identifying the early signs of dementia
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2020-10-01
description Identifying the symptoms of the early stages of dementia is a difficult task, particularly for older adults living in residential care. Internet of Things (IoT) and smart environments can assist with the early detection of dementia, by nonintrusive monitoring of the daily activities of the older adults. In this work, we focus on the daily life activities of adults in a smart home setting to discover their potential cognitive anomalies using a public dataset. After analysing the dataset, extracting the features, 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 efficient model to identify the cognitive status of monitored adults. Using our predictive model and our approach of distinctive feature selection, we have achieved 90.74% accuracy in detecting the onset of dementia.
topic dementia
internet of things
machine learning
IoT in healthcare
IoT in dementia care
dementia and smart environment
url https://www.mdpi.com/1424-8220/20/21/6031
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AT seyedshahrestani internetofthingsandmachinelearningforhealthyageingidentifyingtheearlysignsofdementia
AT honcheung internetofthingsandmachinelearningforhealthyageingidentifyingtheearlysignsofdementia
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