Translating Emotions to Annotations -- A Participant Perspective of Physiological Emotion Data Collection
arXiv:2503.19636 · doi:10.1145/3711093
Abstract
Physiological signals hold immense potential for ubiquitous emotion monitoring, presenting numerous applications in emotion recognition. However, harnessing this potential is hindered by significant challenges, particularly in the collection of annotations that align with physiological changes since the process hinges heavily on human participants. In this work, we set out to study human participant perspectives in the emotion data collection procedure. We conducted a lab-based emotion data collection study with 37 participants using 360 degree virtual reality video stimulus followed by semi-structured interviews with the study participants. Our findings presented that intrinsic factors like participant perception, experiment design nuances, and experiment setup suitability impact their emotional response and annotation within lab settings. Drawing from our findings and prior research, we propose recommendations for incorporating participant context into annotations and emphasizing participant-centric experiment designs. Furthermore, we explore current emotion data collection practices followed by AI practitioners and offer insights for future contributions leveraging physiological emotion data.
Preprint version, Accepted at ACM CSCW 2025
References in corpus (6)
- Improving fairness in machine learning systems: What do industry practitioners need?
- Virtual Reality for Emotion Elicitation -- A Review
- K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations
- How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study
- Lessons Learned from Designing an AI-Enabled Diagnosis Tool for Pathologists
- A Multi-Componential Approach to Emotion Recognition and the Effect of Personality