Boiling Mind-A Dataset of Physiological Signals during an Exploratory Dance Performance

Zhuoqi Fu, Jiawen Han, Dingding Zheng, Moe Sugawa, Taichi Furukawa, Chernyshov George, Hynds Danny, Padovani Marcelo, Marky Karola, Kouta Minamizawa, Jamie A. Ward, Kai Kunze

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


The relationship between audience and performers is crucial to what makes live events so special. The aim of this work is to develop a new approach amplifying the link between audiences and performers. Specifically, we explore the use of wearable sensors in gathering real-time audience data to augment the visuals of a live dance performance. We used the J!NS MEME, smart glasses with integrated electrodes enabling eye movement analysis (e.g. blink detection) and inertial motion sensing of the head (e.g. nodding recognition). This data is streamed from the audience and visualised live on stage during a performance, alongside we also collected heart rate and eye gaze of selected audience. In this paper we present the recorded dataset, including accelerometer, electrooculography(EOG), and gyroscope data from 23 audience members.

Original languageEnglish
Title of host publicationProceedings - AHs 2021
Subtitle of host publicationAugmented Humans Conference 2021
PublisherAssociation for Computing Machinery
Number of pages3
ISBN (Electronic)9781450384285
Publication statusPublished - 2021 Feb 22
Event2021 Augmented Humans Conference, AHs 2021 - Rovaniemi, Finland
Duration: 2021 Feb 222021 Feb 24

Publication series

NameACM International Conference Proceeding Series


Conference2021 Augmented Humans Conference, AHs 2021


  • Audience Engagement
  • Physiological Signals
  • Visualization

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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