Personalization Effect on Emotion Recognition from Physiological Data: An Investigation of Performance on Different Setups and Classifiers
This work addresses emotion recognition for personalized healthcare or human-computer interaction, but it is incremental as it builds on existing methods with a focus on personalization.
The paper tackled emotion recognition from physiological signals by investigating inter-subject variability and personalization effects, resulting in a personalized model that enhances prediction without requiring additional feedback.
This paper addresses the problem of emotion recognition from physiological signals. Features are extracted and ranked based on their effect on classification accuracy. Different classifiers are compared. The inter-subject variability and the personalization effect are thoroughly investigated, through trial-based and subject-based cross-validation. Finally, a personalized model is introduced, that would allow for enhanced emotional state prediction, based on the physiological data of subjects that exhibit a certain degree of similarity, without the requirement of further feedback.