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Pathways to live visuals in dance performances: a quantitative audience study

Pathways to live visuals in dance performances: a quantitative audience study

Masu, Raul and Correia, Nuno N. ORCID: 0000-0002-9999-9162 (2020) Pathways to live visuals in dance performances: a quantitative audience study. EAI Endorsed Transactions on Creative Technologies, 20 (23):e5. ISSN 2409-9708 (Online) (doi:https://doi.org/10.4108/eai.13-7-2018.163987)

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Abstract

INTRODUCTION: We present an audience study investigating the impact of different technologies to create visuals in dance performances.

OBJECTIVES: We investigated four conditions: motion capture, sensors, camera image, and minimal interaction; and four variables: how much did the audience perceive a connection between the body and the visuals; the visuals as merely copying the dancer; how much distracting were the visuals; and how much did the audience enjoy the visuals.

METHODS: We used a questionnaire to collect data. We analyzed it using Friedman’s test, and Spearman’s correlation test.

RESULTS: The audience perceived a stronger connection in the camera condition, but in the same condition, visuals tend to be merely copying the dancer. We also suggest that the perceived connection has a positive correlation with enjoyment, while distraction has a negative correlation.

CONCLUSION: Our results help to highlight the impact that different technology have on live visuals for dance.

Item Type: Article
Additional Information: © EAI Endorsed Transactions on Creative Technologies Author Name et al., licensed to ICST. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/ licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
Uncontrolled Keywords: audience study, dance, live visuals, interaction design
Subjects: N Fine Arts > NX Arts in general
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > Department of Computing & Information Systems
Related URLs:
Last Modified: 29 May 2020 12:22
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/27497

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