Can we say a cat is a cat? Understanding the challenges in annotating physiological signal-based emotion data
This paper presents a position discussion on the current technique of annotating physiological signal-based emotion data and 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.
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.