2026-10-14T15:15:16Zhttps://www.radar-service.eu/oai/OAIHandler

10.35097/9c5yvpn5yqycd23s2026-10-09T10:46:54Zradar4kit

https://dx.doi.org/10.35097/9c5yvpn5yqycd23s Müller, Elias Greiner, Kirsten Benke, Ivo Maedche, Alexander BEmotion: A Real-World Data Collection for Emotion Recognition Using Smartwatches and Lessons Learned Karlsruhe Institute of Technology 2026 Information Technology dataset Dataset info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode 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 \num{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, identify a need for greater personalization in emotion recognition rather than a one-fits-all recognition. ELMU_2023_Q2_BEmotion ELMU_2023_Q2_BEmotion https://ror.org/018mejw64 application/x-tar