Identification of the onset of dementia of older adults in the age of Internet of things

Farhad Ahamed; Seyed Shahrestani; Hon Cheung

doi:10.1109/iCMLDE.2018.00011

Identification of the onset of dementia of older adults in the age of Internet of things

Farhad Ahamed, Seyed Shahrestani, Hon Cheung

Western Sydney University

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

2 Citations (Scopus)

Abstract

![CDATA[Detecting early onset dementia can reduce the rate of deterioration of cognitive ability in the elderly. By collecting data of daily life in older adults using smart devices, machine learning can detect anomalies in the daily or weekly routine. Additionally, smart connected devices can help to identify dementia symptoms in the behaviour of older adults who live independently. This can alleviate the fear and concerns of the older adults and caregiver, and through selfmanagement, can gradually increase their overall quality of life. This paper develops these ideas.]]

Original language	English
Title of host publication	Proceedings of 2018 International Conference on Machine Learning and Data Engineering (iCMLDE 2018), 3-7 December 2018, Sydney, Australia
Publisher	IEEE
Pages	1-7
Number of pages	7
ISBN (Print)	9781728104041
DOIs	https://doi.org/10.1109/iCMLDE.2018.00011
Publication status	Published - 2019
Event	International Conference on Machine Learning and Data Engineering - Duration: 3 Dec 2018 → …

Conference

Conference	International Conference on Machine Learning and Data Engineering
Period	3/12/18 → …

Keywords

Internet of things
dementia
machine learning

Access to Document

10.1109/iCMLDE.2018.00011

Cite this

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title = "Identification of the onset of dementia of older adults in the age of Internet of things",

abstract = "![CDATA[Detecting early onset dementia can reduce the rate of deterioration of cognitive ability in the elderly. By collecting data of daily life in older adults using smart devices, machine learning can detect anomalies in the daily or weekly routine. Additionally, smart connected devices can help to identify dementia symptoms in the behaviour of older adults who live independently. This can alleviate the fear and concerns of the older adults and caregiver, and through selfmanagement, can gradually increase their overall quality of life. This paper develops these ideas.]]",

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author = "Farhad Ahamed and Seyed Shahrestani and Hon Cheung",

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language = "English",

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pages = "1--7",

booktitle = "Proceedings of 2018 International Conference on Machine Learning and Data Engineering (iCMLDE 2018), 3-7 December 2018, Sydney, Australia",

publisher = "IEEE",

note = "International Conference on Machine Learning and Data Engineering ; Conference date: 03-12-2018",

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Ahamed, F , Shahrestani, S & Cheung, H 2019, Identification of the onset of dementia of older adults in the age of Internet of things. in Proceedings of 2018 International Conference on Machine Learning and Data Engineering (iCMLDE 2018), 3-7 December 2018, Sydney, Australia. IEEE, pp. 1-7, International Conference on Machine Learning and Data Engineering, 3/12/18. https://doi.org/10.1109/iCMLDE.2018.00011

Identification of the onset of dementia of older adults in the age of Internet of things. / Ahamed, Farhad ; Shahrestani, Seyed; Cheung, Hon.
Proceedings of 2018 International Conference on Machine Learning and Data Engineering (iCMLDE 2018), 3-7 December 2018, Sydney, Australia. IEEE, 2019. p. 1-7.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

TY - GEN

T1 - Identification of the onset of dementia of older adults in the age of Internet of things

AU - Ahamed, Farhad

AU - Shahrestani, Seyed

AU - Cheung, Hon

PY - 2019

Y1 - 2019

N2 - ![CDATA[Detecting early onset dementia can reduce the rate of deterioration of cognitive ability in the elderly. By collecting data of daily life in older adults using smart devices, machine learning can detect anomalies in the daily or weekly routine. Additionally, smart connected devices can help to identify dementia symptoms in the behaviour of older adults who live independently. This can alleviate the fear and concerns of the older adults and caregiver, and through selfmanagement, can gradually increase their overall quality of life. This paper develops these ideas.]]

AB - ![CDATA[Detecting early onset dementia can reduce the rate of deterioration of cognitive ability in the elderly. By collecting data of daily life in older adults using smart devices, machine learning can detect anomalies in the daily or weekly routine. Additionally, smart connected devices can help to identify dementia symptoms in the behaviour of older adults who live independently. This can alleviate the fear and concerns of the older adults and caregiver, and through selfmanagement, can gradually increase their overall quality of life. This paper develops these ideas.]]

KW - Internet of things

KW - dementia

KW - machine learning

UR - http://handle.westernsydney.edu.au:8081/1959.7/uws:49819

U2 - 10.1109/iCMLDE.2018.00011

DO - 10.1109/iCMLDE.2018.00011

M3 - Conference Paper

SN - 9781728104041

SP - 1

EP - 7

BT - Proceedings of 2018 International Conference on Machine Learning and Data Engineering (iCMLDE 2018), 3-7 December 2018, Sydney, Australia

PB - IEEE

T2 - International Conference on Machine Learning and Data Engineering

Y2 - 3 December 2018

ER -

Identification of the onset of dementia of older adults in the age of Internet of things

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Conference

Keywords

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