Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.548236
Title: Detecting and monitoring behavioural change through personalised ambient monitoring
Author: Amor, James D.
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2011
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Abstract:
Bipolar disorder (BD) is one form of mental illness and is estimated to affect around 0.4{1.6% of the population. The disorder is characterised by recurrent episodes of mania and depression and is estimated to cost the UK economy £5.21 billion a year. Many people with BD self-monitor their behaviour to help them identify the early warning signs of an affective episode. The Personalised Ambient Monitoring (PAM) project has been conceived take ideas from existing telehealth systems and apply them to BD. By using a distributed network of discreet, unobtrusive sensors, the user's behavioural patterns can be monitored and deviations in their behaviour can be detected. In doing so it is hoped that the early warning signs can be detected and that this can be used to assist them in their self-monitoring. The PAM system is being developed by a multi-disciplinary team based at the ISVR and the School of Management at the University of Southampton, the School of Electrical and Electronic Engineering at the University of Nottingham and the Department of Computing Science and Mathematics at the University of Stirling. This thesis presents the background and motivations for the PAM project, the approach the project will take, a review of appropriate data analysis techniques and the experimental work that has been undertaken in the investigation of accelerometry for activity monitoring, the use of a wireless camera to monitor a complex environment and the use of multiple sensors to capture behaviour patterns in a technical trial. Results from the technical trial show that it is possible to process information from a variety of sensors to identity activity signatures and behavioural patterns in normal controls. When these activity patterns are trained on week-days, the results presented show that it is possible to identify weekend days as being behaviourally different.
Supervisor: James, C. J. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.548236  DOI: Not available
Keywords: RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry ; TK Electrical engineering. Electronics Nuclear engineering
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