Print Email Facebook Twitter Towards Analyzing and Predicting the Experience of Live Performances with Wearable Sensing Title Towards Analyzing and Predicting the Experience of Live Performances with Wearable Sensing Author Gedik, E. (TU Delft Pattern Recognition and Bioinformatics) Cabrera Quiros, L.C. (TU Delft Pattern Recognition and Bioinformatics) Martella, Claudio (Vrije Universiteit Amsterdam) Englebienne, Gwenn (University of Twente) Hung, H.S. (TU Delft Pattern Recognition and Bioinformatics) Date 2018 Abstract We present an approach to interpret the response of audiences to live performances by processing mobile sensor data. We apply our method on three different datasets obtained from three live performances, where each audience member wore a single tri-axial accelerometer and proximity sensor embedded inside a smart sensor pack. Using these sensor data, we developed a novel approach to predict audience members' self-reported experience of the performances in terms of enjoyment, immersion, willingness to recommend the event to others and change in mood. The proposed method uses an unsupervised method to identify informative intervals of the event, using the linkage of the audience members' bodily movements, and uses data from these intervals only to estimate the audience members' experience. We also analyze how the relative location of members of the audience can affect their experience and present an automatic way of recovering neighborhood information based on proximity sensors. We further show that the linkage of the audience members' bodily movements is informative of memorable moments which were later reported by the audience. Subject AccelerometersaccelerometersAppraisalartsAtmospheric measurementsaudience responseCouplingsdanceHuman behaviourMotion picturesPhysiologyproximity sensingSensorswearable sensors To reference this document use: http://resolver.tudelft.nl/uuid:3f2203cd-f404-4dd4-ad88-f487c0d7d555 DOI https://doi.org/10.1109/TAFFC.2018.2875987 Embargo date 2022-04-08 ISSN 1949-3045 Source IEEE Transactions on Affective Computing, PP (99), 1-8 Bibliographical note Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type journal article Rights © 2018 E. Gedik, L.C. Cabrera Quiros, Claudio Martella, Gwenn Englebienne, H.S. Hung Files PDF Towards_Analyzing_and_Pre ... ensing.pdf 771.99 KB Close viewer /islandora/object/uuid:3f2203cd-f404-4dd4-ad88-f487c0d7d555/datastream/OBJ/view