Support dementia: using wearable assistive technology and analysing real-time data

Abstract

Within healthcare, Complex Event Processing (CEP) engines can analyse events and related data which come from various sources (wearable sensors, environment sensors etc.) in real-time and provide insights for a better healthcare. A major strength of CEP is the automated matching of patterns and triggering of immediate actions. Dementia is becoming increasingly common in the elderly population. Currently, care provided by the NHS is in the form of personal attendants such as nurses and social workers. Reducing the amount of personal care devoted to early Dementia sufferers by means of remotely monitoring their condition will reduce the pressure on NHS resources and will promote good quality independent living. The use of sensory devices to monitor the activities of daily living of early Dementia suffers, and then analyse the sensory output to identify deviations from normal behavioural patterns can indicate deterioration in the Dementia condition such as restlessness and wandering, and subsequently seek intervention from the caregivers or the health clinicians. Controlling healthcare demand through prevention or delay is essential and technologies together with data analytics will play an increasing role. The main focus of this paper is to provide a broad overview of how people with special needs such as dementia patients, will benefit from assisted technologies to overcome barriers in achieving their daily activities and to present how CEP engines for real-time analytics can support this. This work will feed into fulfilling research work which is to provide a suitable framework to accurately analyse real-time data from assistive technology and wearable devices for remote healthcare, particularly dementia