PaGER-Sync ADICVIDEO: Player affective gaming experience & responses - synchronized ADICVIDEO dataset.

Area of Science:

• Affective Computing
• Human-Computer Interaction
• Behavioral Analysis

Background:

• Realistic home gaming environments require multimodal data for accurate affect detection.
• Existing datasets may lack synchronized physiological and facial expression data during immersive gaming.
• Understanding the interplay between psychological traits and real-time emotional responses is crucial.

Purpose of the Study:

• Introduce the PaGER-Sync ADICVIDEO dataset, a novel multimodal repository.
• Facilitate research in affective computing and human-computer interaction.
• Enable development and evaluation of multimodal affect detection models.

Main Methods:

• Collected synchronized physiological data (EDA, BVP, TEMP) using Empatica E4 wristbands.
• Captured facial expressions using FaceReader software during video game sessions.
• Integrated psychometric questionnaire scores and demographic data for psychological context.

Main Results:

• The dataset contains frame-level synchronized physiological and facial expression data from 25 participants.
• Data is segmented by game (Tetris, Sonic Racing, Fall Guys) and includes strongest emotion summaries.
• Provides a comprehensive resource for multimodal affect detection model development.

Conclusions:

• The PaGER-Sync ADICVIDEO dataset is a valuable resource for studying emotional responses during immersive video gaming.
• Enables advanced research in multimodal affect detection and the influence of psychological factors.
• Supports the creation of more sophisticated human-computer interaction systems.