Multi-scale Transformer-based Network for Emotion Recognition from Multi Physiological Signals

Tu Vu, Van Thong Huynh, Soo-Hyung Kim
5/1/2023
eess.SPcs.AIcs.LG

No official code found. Best implementation: cprl-team/EPiC-2023-ACII (2 stars, CC-BY-4.0, last commit 2024-10-16).

Abstract

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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Code Implementations(1)

cprl-team/EPiC-2023-ACIICommunity100%
CC-BY-4.0

EPiC 2023 competition

20Shell, Python2 years agoCC-BY-4.0

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Cite this paper

@article{vu2023multiscale,
  title  = {Multi-scale Transformer-based Network for Emotion Recognition from Multi Physiological Signals},
  author = {Tu Vu and Van Thong Huynh and Soo-Hyung Kim},
  year   = {2023},
  eprint = {2305.00769},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.00769v2}
}

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