# Can we say a cat is a cat? Understanding the challenges in annotating physiological signal-based emotion data

arXiv: https://arxiv.org/abs/2406.14908 · HTML: https://mlsanity.com/paper/2406.14908/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2024.

## Built on by (most cited first, 1 of 1)

- [AnnoSense: A Framework for Physiological Emotion Data Collection in Everyday Settings for AI](https://mlsanity.com/paper/2508.02680/) (2025)

## Abstract (arXiv)

Artificial Intelligence (AI) algorithms, trained on emotion data extracted from physiological signals, provide a promising approach to monitoring emotions, affect, and mental well-being. However, the field encounters challenges because there is a lack of effective methods for collecting high-quality data in everyday settings that genuinely reflect changes in emotion or affect. This paper presents a position discussion on the current technique of annotating physiological signal-based emotion data. Our discourse underscores the importance of adopting a nuanced understanding of annotation processes, paving the way for a more insightful exploration of the intricate relationship between physiological signals and human emotions.