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Internet of Things Enabled Technologies for Behaviour Analytics in Elderly Person Care: A Survey

Newcombe, LS, Yang, P, Carter, CJ and Hanneghan, MB Internet of Things Enabled Technologies for Behaviour Analytics in Elderly Person Care: A Survey. In: Proceedings of IEEE Smart Data (SmartData) (iThings-GreenCom-CPSCom-SmartData 2017) . (IEEE Smart Data (SmartData) (iThings-GreenCom-CPSCom-SmartData 2017), 21-23 June 2017, Exeter, Devon, UK). (Accepted)

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The advances in sensor technology over recent years has provided new ways for researchers to monitor the elderly in uncontrolled environments. Sensors have become smaller, cheaper and can be worn on the body, potentially creating a network of sensors. Smart phones are also more common in the average household and can also provide some behavioural analysis due to the built in sensors. As a result of this, researchers are able to monitor behaviours in a more natural setting, which can lead to more useful data. This is important for those that may be suffering from mental illness as it allows for continuous, non-invasive monitoring in order to diagnose symptoms from different behaviours. However there are various challenges that need to be addressed ranging from issues with sensors to the involvement of human factors. It is vital that these challenges are taken into consideration along with the major behavioural symptoms that can appear in an Elderly Person. For a person suffering with Dementia, the application of sensor technologies can improve the quality of life of the person and also monitor the progress of the disease through behavioural analysis. This paper will consider the behaviours that can be associated with dementia and how these behaviours can be monitored through sensor technology. We will also provide an insight into some sensors and algorithms gathered through survey in order to provide advantages and disadvantages of these technologies as well as to present any challenges that may face future research.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Internet of things; Healthcare; Behaviour Analytics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RT Nursing
Divisions: Computer Science & Mathematics
Publisher: IEEE
Date Deposited: 18 Aug 2017 10:39
Last Modified: 13 Apr 2022 15:15
URI: https://researchonline.ljmu.ac.uk/id/eprint/6948
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