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Understanding emotional and behavioural responses through physiological signal analysis recorded during theatrical performance

Among various cultural interventions, theatre offers a powerful medium for emotional expression, reflection, and social connection. Live performances can positively influence mental well-being. Therefore, studying audience responses during theatrical performances can provide valuable insights into the relationship between theatre, emotional experiences, and well-being. Physiological responses have emerged as an important modality for identifying patterns associated with different emotional states. However, in naturalistic environments such as live performances, analysing these signals is challenging due to the absence of explicit labels. In this study, we focus on understanding emotional and behavioural responses using physiological data collected during a live theatrical performance. Data were recorded from participants using multiple sensing modalities. In addition, a comprehensive analytical framework was employed, which included demographic analysis, visual exploration of physiological signals, participant-level observations, scene-wise physiological analysis, and intensity-based analysis using z-score normalization to summarize the findings. The findings revealed that dramatic, surprising, and intense scenes produced the strongest activation, often with concurrent increases in HR and SCL, whereas empathy-driven scenes showed lower and more stable responses. Humorous scenes elicited moderate-to-high activation, particularly in the presence of sudden reactions or loud sounds. Analyses also identified shared physiological response patterns across participants, with activation concentrated around key emotional events. These results indicate that combined HR and SCL measures can effectively capture variations in audience engagement during live theatrical performances.

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Emotion Recognition from Physiological Signals Using Machine Learning Algorithms Under Controlled Emotional Stimuli

Emotion recognition using physiological signals plays a crucial role in well-being analysis, affective computing and human-computer interaction. This study investigates the performance of multiple machine learning models in classifying targets such as discrete emotions with varying granularity, valence and arousal using physiological signals such as Electrocardiogram (ECG) and Galvanic Skin Response (GSR). In here, we extracted various time- and frequency-domain features from the ECG and GSR data to train machine learning models. The results indicate that categorizing discrete emotions with fewer emotions and categorizing arousal achieves good classification accuracy with tree-based models. XGBoost achieved accuracy of 52.8 % for classifying discrete emotions and Random Forest achieved accuracy of 53.6% for classifying arousal. In both the cases, the combination of ECG+GSR feature sets achieved best performance. These findings highlight the effectiveness of physiological signals in capturing emotional states and support their use for emotion recognition systems.

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AI-Driven Analysis of the Effects of Recreational Activities to Well-Being using Physiological Responses: A survey

Well-being is a broader concept ensuring psychological, social, physical, and cognitive health. Recreational activities are one of the major approaches to facilitate well-being. There are various recreational activities in each of the domains in which well-being can be measured. Although well-being is often assessed using subjective scales, physiological responses such as heart rate, heart rate variability, skin responses and brain activity data provide objective ways to estimate the level of well-being. Research has focused on using these measurable physiological signals to develop AI driven well-being analysis concepts. This study presents a review of previous studies that have considered analyzing well-being. This review summarizes studies on the basis of recreational activities, data collection scenarios, subjective and objective data, data analysis methods and evaluation criteria. Furthermore, this study also proposes a framework to enhance data collection, analysis and visualization setup for the assessment of well-being.

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