The Cold Start Problem and Per-Group Personalization in Real-Life Emotion Recognition With Wearables

Emotion recognition in real life from physiological signals provided by wrist worn devices still remains a great challenge especially due to difficulties with gathering annotated emotional events. For that purpose, we suggest building pre-trained machine learning models capable of detecting intense emotional states. This work aims to explore the cold start problem, where no data from the target subjects (users) are available at the beginning of the experiment to train the reasoning model. To address this issue, we investigate the potential of per-group personalization and the amount of data needed to perform it. Our results on real-life data indicate that even a week’s worth of personalized data improves the model performance.