  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.
