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

arXiv: https://arxiv.org/abs/2305.00769 · HTML: https://mlsanity.com/paper/2305.00769/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 1 May 2023.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting](https://mlsanity.com/paper/1907.00235/) (2019)
- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case](https://mlsanity.com/paper/2001.08317/) (2020)
- [A dataset of continuous affect annotations and physiological signals for emotion analysis](https://mlsanity.com/paper/1812.02782/) (2018)
- [Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognition](https://mlsanity.com/paper/2303.17611/) (2023)
- [Transformer-Based Self-Supervised Learning for Emotion Recognition](https://mlsanity.com/paper/2204.05103/) (2022)
- [A Survey on Physiological Signal Based Emotion Recognition](https://mlsanity.com/paper/2205.10466/) (2022)
- [A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion from Heartbeat](https://mlsanity.com/paper/1902.03043/) (2019)
- [Emotion Recognition with Pre-Trained Transformers Using Multimodal Signals](https://mlsanity.com/paper/2212.13885/) (2022)

## Built on by (most cited first, 1 of 1)

- [Efficient-Husformer: Efficient Multimodal Transformer Hyperparameter Optimization for Stress and Cognitive Loads](https://mlsanity.com/paper/2511.22362/) (2025)

## Abstract (arXiv)

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.