Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

Experimental Analysis of Cost-Effective Mobile Sensing Technologies for Activity Analytics in Elderly Care

Newcombe, LS, Yang, P, Carter, C, Hanneghan, M and Qi, J Experimental Analysis of Cost-Effective Mobile Sensing Technologies for Activity Analytics in Elderly Care. In: IEEE Conference Proceedings . (The 16th International Conference on Smart City, 28 June 2018 - 30 June 2018, Exeter, UK). (Accepted)

[img] Text
Experimental Analysis of Cost-Effective Mobile Sensing Technologies for Activity Analytics in Elderly Care.pdf - Published Version
Restricted to Repository staff only

Download (146kB)

Abstract

Advancements in sensor technology has provided new ways for researchers to monitor the elderly in uncontrolled environments. Sensors have become smaller, cheaper and can now be worn on the body. Smart phones are also more common in the average household and can provide some analysis of behaviour. Because of this, researchers are able to monitor behaviours in a more natural setting, which can produce useful data. For those suffering with a mental illness, this is important as it allows for continuous, non-invasive monitoring in order to diagnose symptoms from different behaviours. However, issues with the sensors and the involvement of human factors are challenges that need to be addressed. These challenges must be taken into consideration in addition to the behavioural symptoms of Dementia that can appear in the elderly. The application of sensor technologies can aid in improving the quality of life of an elderly person with Dementia and monitor the progression of the disease through behavioural analysis. This paper will provide an experiment protocol that can be used to monitor those with mild cognitive impairment in a natural environment. We will also provide data and results from an initial experiment and discuss our plans for future experimentation.

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
Divisions: Computer Science
Date Deposited: 18 Jul 2018 10:30
Last Modified: 18 Jul 2018 10:30
URI: http://researchonline.ljmu.ac.uk/id/eprint/8972

Actions (login required)

View Item View Item