Smart Monitoring: An Intelligent System to Facilitate Health Care across an Ageing Population

Abstract

In the UK, the number of people living with self-limiting conditions, such as Dementia, Parkinson’s disease and depression, is increasing. The resulting strain on national healthcare resources means that providing 24-hour monitoring for patients is a challenge. As this problem escalates, caring for an ageing population will become more demanding over the next decade. Our research directly proposes an alternative and cost effective method for supporting independent living that offers enhancements for Early Intervention Practices (EIP). In the UK, a national roll out of smart meters is underway, which enable detailed around-the-clock monitoring of energy usage. This granular data captures detailed habits and routines through the users’ interactions with electrical devices. Our approach utilises this valuable data to provide an innovative remote patient monitoring system. The system interfaces directly with a patient’s smart meter, enabling it to distinguish reliably between subtle changes in energy usage in real-time. The data collected can be used to identify any behavioural anomalies in a patient’s habit or routine, using a machine learning approach. Our system utilises trained models, which are deployed as web services using cloud infrastructures, to provide a comprehensive monitoring service. The research outlined in this paper demonstrates that it is possible to classify successfully both normal and abnormal behaviours using the Bayes Point Machine binary classifier