Physiological measures of audience engagement and interpersonal synchrony during an immersive participatory performance
Bibliographic record
Abstract
The advent of immersive technologies has shifted the creative landscape in the performing arts, presenting new opportunities for audience interaction and engagement. While physiological measures have previously been used to evaluate audience’s emotional and attentional responses in performances, the mediation of audience participation through augmented reality interfaces provides a new context in which we can investigate individual and collective experiences. This study explores the feasibility of autonomous nervous system recording as a measure of audience engagement and group synchrony during a participatory immersive performance prototype that integrates circus, movement, music and interactive technology. Wearable sensors, connected by Bluetooth, were used to record electrodermal activity fingertip temperature and heart rate variability in 20 participants. We obtained continuous and reliable data from twelve participants, and our analysis revealed distinct physiological responses corresponding to different performance segments, highlighting moments of high arousal and engagement during interactive activities. The mean single session index for electrodermal activity was calculated as a measure of group synchrony, showing trends towards differences across show segments. Overall, we demonstrate the feasibility of using physiological signal recording to investigate individual and collective audience experiences in participatory and immersive performances and discuss important considerations for research-creation collaborations. L’avènement des technologies immersives a modifié le paysage créatif des arts du spectacle. Ces innovations offrent de nouvelles opportunités en matière d’interaction et de participation du public. Par le passé, des mesures physiologiques ont permis d’évaluer la réaction de l’assistance en termes d’émotion et d’attention lors de performances. Mais la médiation de la participation du public par le biais de la réalité augmentée propose un contexte inédit pour analyser des expériences individuelles et collectives. Cette étude explore la faisabilité de prendre en compte l’enregistrement du système nerveux autonome pour mesurer la participation du public et la synchronie de groupe durant une performance immersive et participative type intégrant le cirque, le mouvement, la musique et les technologies interactives. Différentes données ont été collectées parmi un panel de 20 participant·e·s grâce à des capteurs portables connectés par Bluetooth : l’activité électrodermale, la température à l’extrémité du doigt et la variabilité de fréquence cardiaque. Nous avons obtenu des informations fiables et continues avec douze participant·e·s. De plus, notre analyse a révélé des réactions physiologiques distinctes correspondant à différents segments de la performance, mettant en valeur des moments de stimulation élevée et de forte implication pendant les interventions interactives. On a calculé l’indice moyen de session unique (single session index – SSI) pour les signaux de l’activité électrodermale afin de mesurer la synchronie de groupe, montrant des écarts entre des segments du spectacle. Dans l’ensemble, nous démontrons la faisabilité du recours à l’enregistrement de signaux physiologiques pour étudier des expériences individuelles et collectives d’un public lors de performances participatives et immersives, ainsi que pour discuter d’éléments importants dans le cadre de collaborations entre recherche et création.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".