Psychophysiological Data for Affect Recognition in VR Exergaming Experiments

Datasets and analyses for the CHI 2020 paper 'Affect Recognition using Psychophysiological Correlates in High Intensity VR Exergaming' by Soumya C. Barathi of the University of Bath. The data comprises two experiments investigating sensor-based affect recognition during different VR exergaming scenarios, including conventional exercise, sedentary VR gaming, and optimal/overwhelming game conditions. The release includes CSV data sheets, JASP files with statistical tests, and R scripts for correlation and regression analyses.

Use Cases

• Training affect recognition models based on psychophysiological measures like gaze fixations and skin conductivity.
• Comparing the impact of physical exertion versus gamification on physiological signals during VR activities.
• Analyzing the utility of pupil diameter and eye blinks for determining affective valence and arousal states.
• Benchmarking sensor performance for emotion detection in underwhelming, overwhelming, and optimal VR exergaming scenarios.

Strengths

• Includes data from two distinct experiments with defined conditions (rest, exercise, VR exergaming, sedentary VR gaming).
• Provides associated statistical analysis files (JASP for ANOVAs/t-tests) and R scripts for correlation/regression.
• Focuses on specific, research-validated psychophysiological measures: gaze fixations, eye blinks, pupil diameter, and skin conductivity.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

University of Bath, associated with a CHI 2020 research paper.

Collection Method

Collected from controlled experiments comparing psychophysiological measurements across different VR and exercise scenarios.