Towards Analyzing and Predicting the Experience of Live Performances with Wearable Sensing

Ekin Gedik, Laura Cabrera-Quiros, Claudio Martella, Gwenn Englebienne, Hayley Hung

Research output: Contribution to journalArticleScientificpeer-review

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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.

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalIEEE Transactions on Affective Computing
VolumePP
Issue number99
DOIs
Publication statusE-pub ahead of print - 2018

Keywords

  • Accelerometers
  • accelerometers
  • Appraisal
  • arts
  • Atmospheric measurements
  • audience response
  • Couplings
  • dance
  • Human behaviour
  • Motion pictures
  • Physiology
  • proximity sensing
  • Sensors
  • wearable sensors

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