Lessons Learned from Developing Emotion Recognition System for Everyday Life

Recognizing emotions in everyday life requires a user-friendly and reliable system based on a smartphone and wearables. For over a year, we have been developing the Emognition system, which enables collecting emotionally annotated physiological signals in real-life scenarios. In this work, we describe the system architecture, the components and libraries used, as well as the development, testing, and implementation strategies. We explain in detail the integration with wearables - smartwatch Samsung Galaxy Watch 3 and chest strap Polar H10. The encountered problems and developed solutions are thoroughly discussed. We also provide the advantages and limitations of several frameworks for embedding machine learning models into a resource-restricted mobile application.