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The Drive to Explore: Physiological Computing in a Cultural Heritage Context

Chapter
Part of the Human–Computer Interaction Series book series (HCIS)

Abstract

Contemporary heritage institutions model installations and artefacts around a passive receivership where content is consumed but not influenced by visitors. Increasingly, heritage institutions are incorporating ubiquitous technologies to provide visitors with experiences that not only transfer knowledge but also entertain. This poses the challenge of how to incorporate technologies into exhibits to make them more approachable and memorable, whilst preserving cultural salience. We present work towards an adaptive interface which responds to a museum visitor’s level of interest, in order to deliver a personalised experience through adaptive curation within a cultural heritage installation. The interface is realised through the use of psychophysiological measures, physiological computing and a machine learning algorithm. We present studies which serve to illustrate how entertainment, education, aesthetic experience and immersion, identified as four factors of visitor experience, can be operationalised through a psychological construct of “interest”. Two studies are reported which take a subject-dependent experimental approach to record and classify psychophysiological signals using mobile physiological sensors and a machine learning algorithm. The results show that it is possible to reliably infer a state of interest from cultural heritage material, informing future work for the development of a real-time physiological computing system for use within an adaptive cultural heritage experience. We propose a framework for a potential adaptive system for cultural heritage based upon story telling principles and an operationalised model of the “knowledge emotion” interest.

Keywords

Computational Physiology CH Context CH Experience Current Adaption Visitor Experience 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was supported by EU FP7 project No.270318 (ARtSENSE).

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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.School of Natural Science and PsychologyLiverpool John Moores UniversityLiverpoolUK

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