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.

翻译：本文提出了一种高效的多尺度Transformer方法，用于从生理数据中识别情感任务。由于现代传感器和机器学习技术能够从这些信号中提取海量信息，该任务已获得研究界的广泛关注。我们的方法结合多模态技术与数据缩放技术，以建立体内信号与人类情感之间的关联。此外，我们利用Transformer和高斯变换技术来提升信号编码效率及整体性能。模型在EPiC竞赛的CASE数据集上取得了良好效果，均方根误差（RMSE）得分为1.45。