  This paper presents an efficient Multi-scale Transformer-based approach for
the task of Emotion recognition from Physiological data, which has gained
widespread attention in the research community due to the vast amount of
information that can be extracted from these signals using modern sensors and
machine learning techniques. Our approach involves applying a Multi-modal
technique combined with scaling data to establish the relationship between
internal body signals and human emotions. Additionally, we utilize Transformer
and Gaussian Transformation techniques to improve signal encoding effectiveness
and overall performance. Our model achieves decent results on the CASE dataset
of the EPiC competition, with an RMSE score of 1.45.
