Dataset for "Sweating the Details: Emotion Recognition and the Influence of Physical Exertion in Virtual Reality Exergaming" and EmoSense SDK

All the raw participant data that was used for the EmoSense SDK and the CHI 2024 paper "Sweating the Details: Emotion Recognition and the Influence of Physical Exertion in Virtual Reality Exergaming". There is data for 72 participants.

Each participant has 10 csv files: 2 aggregated calibration, 2 raw calibration, 3 raw study - These files contain sensor measures under different conditions - and 3 affect response files - participant ground truth measures of affect.

Questionnaire_Data contains post exercise-bout measures of intrinsic motivation and flow.

Baseline_Affect_Data is prestudy participant ground truth affect measures.

Aggregated_Data contains an aggregate file of all participant data that was used for the analysis described in the CHI paper.

Keywords:
virtual reality, exergaming, emotion recognition, affect recognition, physiological sensing, physiological correlates, psychophysiological correlates, high-intensity exercise
Subjects:
Information and communication technologies
Psychology

Cite this dataset as:
Potts, D., Hartley, J., Jicol, C., Clarke, C., Lutteroth, C., 2024. Dataset for "Sweating the Details: Emotion Recognition and the Influence of Physical Exertion in Virtual Reality Exergaming" and EmoSense SDK. Bath: University of Bath Research Data Archive. Available from: https://doi.org/10.15125/BATH-01372.

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Access on request: Access to bona fide researchers only.

EmoSense SDK and Virtual Environments

Creators

Dominic Potts
University of Bath

Joe Hartley
University of Bath

Crescent Jicol
University of Bath

Contributors

University of Bath
Rights Holder

Documentation

Data collection method:

For the methodology and apparatus of the data collection, please refer to the paper.

Technical details and requirements:

The VR exergame developed for this study, required participants to cycle on a stationary Wahoo KICKR exercise bike while wearing a Vive Pro Eye VR headset. Physiological measures were collected using the eye tracker in the VR headset (pupillometry), a Shimmer3 GSR+ tethered to a participant's middle and ring finger (EDA), a Polar H10 HR monitor chest strap (HR and HRV), and a Vive face tracker (facial tracking). All physiological measures were sent to a PC (Intel 13900K, Nvidia GTX 4090 and 64GB of DDR5 RAM) running the Unity VR exergame over Bluetooth (BLE protocol), which recorded all measures at a sample rate of 40-50 Hz using the EmoSense.

Funders

HORIZON EUROPE

EMIL: The European Media and Immersion Lab
101070533

EMIL: The European Media and Immersion Lab
101070533

Publication details

Publication date: 11 May 2024
by: University of Bath

Version: 1

DOI: https://doi.org/10.15125/BATH-01372

URL for this record: https://researchdata.bath.ac.uk/id/eprint/1372

Related papers and books

Potts, D., Broad, Z., Sehgal, T., Hartley, J., O'Neill, E., Jicol, C., Clarke, C., and Lutteroth, C., 2024. Sweating the Details: Emotion Recognition and the Influence of Physical Exertion in Virtual Reality Exergaming. In: Proceedings of the CHI Conference on Human Factors in Computing Systems. ACM. Available from: https://doi.org/10.1145/3613904.3642611.

Contact information

Please contact the Research Data Service in the first instance for all matters concerning this item.

Contact person: Dominic Potts

Departments:

Faculty of Science
Computer Science

Research Centres & Institutes
REal and Virtual Environments Augmentation Labs (REVEAL)