Translating Emotions to Annotations: A Participant's Perspective of Physiological Emotion Data Collection

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 participants' perspectives in the emotion data collection procedure. We conducted a lab-based emotion data collection study with 37 participants using 360° virtual reality video stimulus followed by semi-structured interviews with the study participants. Our findings presented that intrinsic factors like participants' 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 participants' 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.

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Publication Details

Journal
Proceedings of the ACM on Human-Computer Interaction
Published
2025-05-02
DOI
https://doi.org/10.1145/3711093
Citations
3
Primary Topic
Emotion and Mood Recognition
Type
article
Field-Weighted Citation Impact
6.35
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article

Translating Emotions to Annotations: A Participant's Perspective of Physiological Emotion Data Collection

Ritvik Budhiraja, Pankaj Jalote, Pragya Singh, Mohan Kumar et al.
3 citations
Proceedings of the ACM on Human-Computer Interaction
Emotion and Mood Recognition
6.35
article

Translating Emotions to Annotations: A Participant's Perspective of Physiological Emotion Data Collection

Ritvik Budhiraja, Pankaj Jalote, Pragya Singh, Mohan Kumar, Pushpendra Singh
article en
3 citations

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 participants' perspectives in the emotion data collection procedure. We conducted a lab-based emotion data collection study with 37 participants using 360° virtual reality video stimulus followed by semi-structured interviews with the study participants. Our findings presented that intrinsic factors like participants' 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 participants' 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.

Proceedings of the ACM on Human-Computer InteractionVol. 9(2)
Indraprastha Institute of Information Technology Delhi (IN), Rochester Institute of Technology (US), Indian Institute of Technology Delhi (IN)
Openalex Percentile: Top 4%
Emotion and Mood Recognition
6.35
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