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Linking Audience Physiology to Choreography

Linking Audience Physiology to Choreography The use of wearable sensor technology opens up exciting avenues for both art and HCI research, providing new ways to explore the invisible link between audience and performer. To be effective, such work requires close collaboration between performers and researchers. In this article, we report on the co-design process and research insights from our work integrating physiological sensing and live performance. We explore the connection between the audience’s physiological data and their experience during the performance, analyzing a multi-modal dataset collected from 98 audience members. We identify notable moments based on HRV and EDA, and show how the audience’s physiological responses can be linked to the choreography. The longitudinal changes in HRV features suggest a strong connection to the choreographer’s intended narrative arc, while EDA features appear to correspond with short-term audience responses to dramatic moments. We discuss the physiological phenomena and implications for designing feedback systems and interdisciplinary collaborations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Computer-Human Interaction (TOCHI) Association for Computing Machinery

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2023 Association for Computing Machinery.
ISSN
1073-0516
eISSN
1557-7325
DOI
10.1145/3557887
Publisher site
See Article on Publisher Site

Abstract

The use of wearable sensor technology opens up exciting avenues for both art and HCI research, providing new ways to explore the invisible link between audience and performer. To be effective, such work requires close collaboration between performers and researchers. In this article, we report on the co-design process and research insights from our work integrating physiological sensing and live performance. We explore the connection between the audience’s physiological data and their experience during the performance, analyzing a multi-modal dataset collected from 98 audience members. We identify notable moments based on HRV and EDA, and show how the audience’s physiological responses can be linked to the choreography. The longitudinal changes in HRV features suggest a strong connection to the choreographer’s intended narrative arc, while EDA features appear to correspond with short-term audience responses to dramatic moments. We discuss the physiological phenomena and implications for designing feedback systems and interdisciplinary collaborations.

Journal

ACM Transactions on Computer-Human Interaction (TOCHI)Association for Computing Machinery

Published: Mar 7, 2023

Keywords: Datasets

References