Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.617390
Title: Exploring the biocybernetic loop : classifying psychophysiological responses to cultural artefacts using physiological computing
Author: Karran, Alexander John
ISNI:       0000 0004 5350 5088
Awarding Body: Liverpool John Moores University
Current Institution: Liverpool John Moores University
Date of Award: 2014
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Abstract:
The aim of this research project was to provide a bio-sensing component for a real-time adaptive technology in the context of cultural heritage. The proposed system was designed to infer the interest or intention of the user and to augment elements of the cultural heritage experience interactively through implicit interaction. Implicit interaction in this context is the process whereby the system observes the user while they interact with artefacts; recording psychophysiological responses to cultural heritage artefacts or materials and acting upon these responses to drive adaptations in content in real-time. Real-time biocybernetic control is the central component of physiological computing wherein physiological data are converted into a control input for a technological system. At its core the bio-sensing component is a biocybernetic control loop that utilises an inference of user interest as its primary driver. A biocybernetic loop is composed of four main stages: inference, classification, adaptation and interaction. The programme of research described in this thesis is concerned primarily with exploration of the inference and classification elements of the biocybernetic loop but also encompasses an element of adaptation and interaction. These elements are explored first through literature review and discussion (presented in chapters 1-5) and then through experimental studies (presented in chapters 7-11).
Supervisor: Fairclough, Stephen H.; Fergus, Paul Sponsor: Liverpool John Moores University
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.617390  DOI: Not available
Keywords: Psychology. Psychophysiology ; Biocybernetics ; Machine Learning ; Classification ; Real-time
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