Next Article in Journal
Compressing Deep Networks by Neuron Agglomerative Clustering
Previous Article in Journal
A Model for Predictive Maintenance Based on Asset Administration Shell
Previous Article in Special Issue
Nonintrusive Fine-Grained Home Care Monitoring: Characterizing Quality of In-Home Postural Changes Using Bone-Based Human Sensing
Article

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

School of Computer, Data and Mathematical Sciences, Western Sydney University, Second Ave, Kingswood 2747, NSW, Australia
*
Author to whom correspondence should be addressed.
This paper is an extended version of the conference paper: Phill, K.R.; Daniel, H.; Md Rezaul, B. Identification of the Onset of Dementia of Older Adults in the Age of Internet of Things. In Proceedings of the 2018 International Conference on Machine Learning and Data Engineering (iCMLDE), Sydney, Australia, 3–7 December 2018; pp. 1–7.
Sensors 2020, 20(21), 6031; https://doi.org/10.3390/s20216031
Received: 18 August 2020 / Revised: 26 September 2020 / Accepted: 22 October 2020 / Published: 23 October 2020
(This article belongs to the Special Issue Smart IoT Systems for Pervasive Healthcare)
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. View Full-Text
Keywords: dementia; internet of things; machine learning; IoT in healthcare; IoT in dementia care; dementia and smart environment dementia; internet of things; machine learning; IoT in healthcare; IoT in dementia care; dementia and smart environment
Show Figures

Figure 1

MDPI and ACS Style

Ahamed, F.; Shahrestani, S.; Cheung, H. Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia. Sensors 2020, 20, 6031. https://doi.org/10.3390/s20216031

AMA Style

Ahamed F, Shahrestani S, Cheung H. Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia. Sensors. 2020; 20(21):6031. https://doi.org/10.3390/s20216031

Chicago/Turabian Style

Ahamed, Farhad, Seyed Shahrestani, and Hon Cheung. 2020. "Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia" Sensors 20, no. 21: 6031. https://doi.org/10.3390/s20216031

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop