Key result
The proposed Multi-scale Transformer-based Network achieved an overall RMSE score of 1.45 for continuous emotion regression using physiological signals on the CASE dataset.
A multi-scale transformer-based architecture using multimodal physiological signals can effectively estimate continuous human emotions.
Advances multimodal physiological emotion estimation; leaves open clinical validation and cardiovascular integration.
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.
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Vu et al. (2023) studied Emotion recognition (n=30). Multi-scale Transformer-based Network was evaluated on RMSE for Valence and Arousal. The proposed Multi-scale Transformer-based Network achieved an overall RMSE score of 1.45 for continuous emotion regression using physiological signals on the CASE dataset.
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