A Real-World Data Collection for Emotion Recognition Using Smartwatches and Lessons Learned
Year: 2026
Type: conference-paper
Abstract: Emotion recognition is important across healthcare, education, and daily life, but real-world deployment remains challenging. Collecting reliable biosignal and self-report data in natural settings poses key methodological and design challenges. We introduce "BEmotion", a dataset that links optical and kinematic biosignals to self-reported valence, arousal, and context. The dataset was collected using smartwatches at three job fairs, involving 44 participants. While real-world datasets are essential, improved methods for collecting and using them are needed. Thus, we also analyze the challenges of real-world data collection encountered in the study. From these insights, we derive recommendations as well as lessons learned, and future use cases. We contribute: (1) with an open dataset and (2) recommendations for future dataset collections in the field. Furthermore, we identify a need for greater personalization in emotion recognition rather than a one-fits-all recognition.
Source: Mensch und Computer (MuC)
Institution: Karlsruhe Institute of Technology
Language: English
Topic: Emotion and Mood Recognition
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7203777993