Abstract
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.
Highlights
Self-management and self-dependent living in a smart environment have become a mere benefit for older adults in the modern socio-economic system
This paper identifies the critical features within the activity data that are more valuable in detecting the onset of dementia through Machine Learning and the Internet of Things (IoT)
The total Instrumental ADL (IADL) activity completion counter and the duration to perform each IADL are essential features that can be extracted from the IoT enabled smart environment
Summary
Self-management and self-dependent living in a smart environment have become a mere benefit for older adults in the modern socio-economic system. IoT sensor-based smart environment can assist in tracking the daily activities of the older adults and may suggest cognitive decline within the subject. IoT driven remote monitoring can assist older adults as a companion to advise on the possible symptoms of dementia and encourage them to visit a clinician. This smart monitoring can enhance the peace of mind of older adults and caregivers in terms of smarter living. Wearable Sensor-Based Monitoring to Identify the Onset of Cognitive Anomaly. The researchers of Parkinson@Home study group examined the feasibility and compliance to collect clinically relevant data for Parkinson disease.
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