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The emergence of an ageing population is a significant public health concern.This has led to an increase in the number of people living with progressiveneurodegenerative disorders like dementia. Consequently, the strain this isplaces on health and social care services means providing 24-hour monitoring isnot sustainable. Technological intervention is being considered, however nosolution exists to non-intrusively monitor the independent living needs ofpatients with dementia. As a result many patients hit crisis point beforeintervention and support is provided. In parallel, patient care relies onfeedback from informal carers about significant behavioural changes. Yet, notall people have a social support network and early intervention in dementiacare is often missed. The smart meter rollout has the potential to change this.Using machine learning and signal processing techniques, a home energy supplycan be disaggregated to detect which home appliances are turned on and off.This will allow Activities of Daily Living (ADLs) to be assessed, such aseating and drinking, and observed changes in routine to be detected for earlyintervention. The primary aim is to help reduce deterioration and enablepatients to stay in their homes for longer. A Support Vector Machine (SVM) andRandom Decision Forest classifier are modelled using data from three testhomes. The trained models are then used to monitor two patients with dementiaduring a six-month clinical trial undertaken in partnership with Mersey CareNHS Foundation Trust. In the case of load disaggregation for appliancedetection, the SVM achieved (AUC=0.86074, Sen=0.756 and Spec=0.92838). Whilethe Decision Forest achieved (AUC=0.9429, Sen=0.9634 and Spec=0.9634). ADLs arealso analysed to identify the behavioural patterns of the occupant whiledetecting alterations in routine.

Detecting Activities of Daily Living and Routine Behaviours in Dementia Patients Living Alone Using Smart Meter Load Disaggregation