A system for collecting emotionally annotated physiological signals in daily life using wearables

Abstract

Several obstacles have to be overcome in order to recognize emotions and affect in daily life. One of them is collecting a large amount of emotionally annotated data necessary to create data-greedy machine learning-based predictive models. Hence, we propose the Emognition system supporting the collection of rich emotional samples in everyday-life scenarios. The system utilizes smart-wearables to record physiological signals unobtrusively and smartphones to gather self-assessments. We have performed a two-week pilot study with 15 participants and devices available on the market to validate the system. The outcomes of the study, alongside the discussion and lessons learned, are provided.