Abstract
Emotion and affect recognition in the uncontrolled everyday-life environment remains challenging. One of its vital problems is collecting numerous annotated emotional samples indispensable for learning the reasoning model. We propose a novel method supporting rich emotional data collection - a pre-trained binary model recognizing physiologically arousing events in real-time and triggering self-assessments at a convenient point in time. An experimental study on 6.000 hours of recorded physiological signals has been performed. The results suggest that we are able to detect emotional events in real-life scenarios to enhance data collection for emotion recognition in the field.