Towards analyzing and predicting the experience of live performances with wearable sensing

Ekin Gedik (Corresponding author), Laura Cabrera-Quiros (Corresponding author), Claudio Martella, Gwenn Englebienne, Hayley Hung (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)


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
Article number8493261
Pages (from-to)269-276
Number of pages8
JournalIEEE Transactions on Affective Computing
Issue number1
Early online date2020
Publication statusPublished - 1 Jan 2021


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


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