Quantifying audience experience in the wild: Heuristics for developing and deploying a biosensor infrastructure in theaters

Wang, Chen; Wong, Judith Ju‐Ming; Zhu, Xintong; Röggla, Thomas; Jansen, Jack; César, Pablo

doi:10.1109/qomex.2016.7498951

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…Lastly, it would also be very beneficial to have a publicly available collection of signals acquired from the same users at various interdependent sessions separated by significant large time intervals over time, thus, allowing a contextual longitudinal analysis of the user emotional experiences. In addition, a research line to explore concerns the exploration of large-scale groups (in large audiences [95], [96]) versus individual experiences, since individuals tend to respond differently if they are alone or in groups.…”

Section: F Emotion Elicitation Datasetsmentioning

confidence: 99%

A Review, Current Challenges, and Future Possibilities on Emotion Recognition Using Machine Learning and Physiological Signals

Wang²,

et al. 2019

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The seminal work on Affective Computing in 1995 by Picard set the base for computing that relates to, arises from, or influences emotions. Affective computing is a multidisciplinary field of research spanning the areas of computer science, psychology, and cognitive science. Potential applications include automated driver assistance, healthcare, human-computer interaction, entertainment, marketing, teaching and many others. Thus, quickly, the field acquired high interest, with an enormous growth of the number of papers published on the topic since its inception. This paper aims to (1) Present an introduction to the field of affective computing though the description of key theoretical concepts; (2) Describe the current state-of-theart of emotion recognition, tracing the developments that helped foster the growth of the field; and lastly, (3) point the literature take-home messages and conclusions, evidencing the main challenges and future opportunities that lie ahead, in particular for the development of novel machine learning (ML) algorithms in the context of emotion recognition using physiological signals.

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Section: F Emotion Elicitation Datasetsmentioning

confidence: 99%

A Review, Current Challenges, and Future Possibilities on Emotion Recognition Using Machine Learning and Physiological Signals

Wang²,

et al. 2019

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“…The wireless function of the RF12 module makes it possible to run user studies with a group of users at the same time, which can be carried out during theatre performances. The sensors have been validated through a number of experiments (Wang et al, 2016). All the sensor slaves simultaneously send packets back to the master sink node, which is connected to a laptop.…”

Section: Gsr Sensorsmentioning

confidence: 99%

How Are We Connected? - Measuring Audience Galvanic Skin Response of Connected Performances

Wang¹,

Zhu²,

Geelhoed

et al. 2016

Proceedings of the 3rd International Conference on Physiological Computing Systems

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Abstract:Accurately measuring the audience response during a performance is a difficult task. This is particularly the case for connected performances. In this paper, we staged a connected performance in which a remote audience enjoyed the performance in real-time. Both objective (galvanic skin response and behaviours) and subjective (interviews) responses from the live and remote audience members were recorded. To capture galvanic skin response, a group of self-built sensors was used to record the electrical conductance of the skin. The results of the measurements showed that both the live and the remote audience members had a similar response to the connected performance even though more vivid artistic artefacts had a stronger effect on the live audience. Some technical issues also influenced the experience of the remote audience. In conclusion we found that the remoteness had little influence on the connected performance.

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Biometric Technology and Ethics: Beyond Security Applications

North‐Samardzic

2019

J Bus Ethics

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Quantifying audience experience in the wild: Heuristics for developing and deploying a biosensor infrastructure in theaters

Abstract: Abstract-Measuring the experience of audience of arts events is

Cited by 6 publications

References 18 publications

A Review, Current Challenges, and Future Possibilities on Emotion Recognition Using Machine Learning and Physiological Signals

A Review, Current Challenges, and Future Possibilities on Emotion Recognition Using Machine Learning and Physiological Signals

How Are We Connected? - Measuring Audience Galvanic Skin Response of Connected Performances

Biometric Technology and Ethics: Beyond Security Applications

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