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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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-10, Vol.20 (21), p.6031
Main Authors: Ahamed, Farhad, Shahrestani, Seyed, Cheung, Hon
Format: Article
Language:English
Subjects:
Adults
Aged
Anomalies
Caregivers
Datasets
Delivery of Health Care
Dementia
Dementia - diagnosis
dementia and smart environment
Global positioning systems
GPS
Healthy Aging
Humans
Internet of Things
IoT in dementia care
IoT in healthcare
Machine Learning
Older people
Parkinson's disease
Personality
Prediction models
Radio frequency identification
Sensors
Smart buildings
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20216031