Multi-scale Transformer-based Network for Emotion Recognition from Multi Physiological Signals
This work addresses emotion recognition for applications in human-computer interaction, but it appears incremental as it builds on existing Transformer and multi-modal techniques.
The paper tackled emotion recognition from physiological signals by proposing a multi-scale Transformer-based network, achieving an RMSE score of 1.45 on the CASE dataset.
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.