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Support provided to sufferers of Dementia by the National Health Service (NHS) is mainly in the form of personal attendants such as nurses and social workers. The main focus of this paper is to present how the use of assistive technologies can help early sufferers of Dementia patients to overcome barriers in achieving their daily activities and to illustrate how data analytics, such as Complex Event Processing (CEP) in real-time can allow better monitoring of these patients. This activity will contribute to 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 monitoring early sufferers of dementia in order to promote good quality independent living.

With advancements in personalised medicine, healthcare delivery systems have moved away from the one-size-fits-all approach towards tailored treatments that meet the needs of individuals and specific subgroups. As nearly onethird of those diagnosed with epilepsy are classed as refractory and are resistant to antiepileptic medication, there is a need for a personalised method of detecting epileptic seizures. Epidemiological studies show that up to 91% of those diagnosed identify one or more triggers as the causation of their seizure onset. These triggers are patient-specific and can affect those diagnosed in different ways dependent on each person's idiosyncratic tolerance and threshold levels. Whilst these triggers are known to induce seizure onset, only a few studies have even considered their use as a preventive component. Therefore, this pilot study investigates the use of patient-specific triggers (PST) in diagnosed epileptics, and whether they can be used as an additional modality when detecting seizures. This study used a precision medicine approach with artificial intelligence (AI), to train and test several patient-specific algorithms that classified epileptic seizures based on the PST of each participant. Experimental results show accuracy, sensitivity, and specificity scores of 94.73%, 96.90% and 93.33% for participant 1 and 96.87%, 96.96% and 96.77% for participant 2, respectively.

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