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.

翻译：基于生理信号提取的情感数据训练的人工智能算法，为监测情绪、情感及心理健康提供了一种前景广阔的方法。然而，该领域面临挑战，因为在日常环境中缺乏有效收集真实反映情绪或情感变化的高质量数据的方法。本文就当前基于生理信号的情感数据标注技术展开立场讨论。我们的论述强调了对标注过程采取细致入微理解的重要性，为更深入地探索生理信号与人类情感之间复杂关系铺平了道路。