  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 progressive
neurodegenerative disorders like dementia. Consequently, the strain this is
places on health and social care services means providing 24-hour monitoring is
not sustainable. Technological intervention is being considered, however no
solution exists to non-intrusively monitor the independent living needs of
patients with dementia. As a result many patients hit crisis point before
intervention and support is provided. In parallel, patient care relies on
feedback from informal carers about significant behavioural changes. Yet, not
all people have a social support network and early intervention in dementia
care is often missed. The smart meter rollout has the potential to change this.
Using machine learning and signal processing techniques, a home energy supply
can be disaggregated to detect which home appliances are turned on and off.
This will allow Activities of Daily Living (ADLs) to be assessed, such as
eating and drinking, and observed changes in routine to be detected for early
intervention. The primary aim is to help reduce deterioration and enable
patients to stay in their homes for longer. A Support Vector Machine (SVM) and
Random Decision Forest classifier are modelled using data from three test
homes. The trained models are then used to monitor two patients with dementia
during a six-month clinical trial undertaken in partnership with Mersey Care
NHS Foundation Trust. In the case of load disaggregation for appliance
detection, the SVM achieved (AUC=0.86074, Sen=0.756 and Spec=0.92838). While
the Decision Forest achieved (AUC=0.9429, Sen=0.9634 and Spec=0.9634). ADLs are
also analysed to identify the behavioural patterns of the occupant while
detecting alterations in routine.
