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The objective of this work is to measure and classify psychophysiological reactivity in response to cultural heritage material presented as visual and audio. We present a data processing and classification framework for the classification of interest. Two studies are reported, adopting a subject-dependent approach to classify psychophysiological signals using mobile physiological sensors and the support vector machine learning algorithm. The results show that it is possible to reliably infer a state of interest from cultural heritage material using psychophysiological feature data and a machine learning approach, informing future work for the development of a real-time physiological computing system for use within an adaptive cultural heritage experience designed to adapt the provision of information to sustain the interest of the visitor.<\/jats:p>","DOI":"10.1145\/2687925","type":"journal-article","created":{"date-parts":[[2015,1,16]],"date-time":"2015-01-16T14:29:58Z","timestamp":1421418598000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["A Framework for Psychophysiological Classification within a Cultural Heritage Context Using Interest"],"prefix":"10.1145","volume":"21","author":[{"given":"Alexander J.","family":"Karran","sequence":"first","affiliation":[{"name":"Liverpool John Moores University, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephen H.","family":"Fairclough","sequence":"additional","affiliation":[{"name":"Liverpool John Moores University, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kiel","family":"Gilleade","sequence":"additional","affiliation":[{"name":"Liverpool John Moores University, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2015,1,14]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Sensorineural hearing loss in adults","author":"Arts H. 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