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

TL;DR

This paper discusses the complexities and challenges of accurately annotating emotion data derived from physiological signals, emphasizing the need for nuanced methods to improve AI emotion recognition.

Contribution

It provides a critical analysis of current annotation techniques for physiological emotion data and advocates for more refined approaches to better capture emotional nuances.

Findings

Highlighting the limitations of current annotation methods

Emphasizing the importance of nuanced annotation processes

Calling for improved data collection techniques in real-world settings

Abstract

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.