Deep Temporal Modeling and Ensemble Fusion for Multimodal Emotion Recognition from Physiological Signals

2026-06-12

AI Summary

This study addresses emotion and stress recognition from multimodal physiological signals to enhance the performance of health monitoring and affective computing systems. Leveraging the WESAD dataset, the work proposes a two-stage fusion framework that integrates early signal fusion at the sensor level with multi-model ensembling at the prediction stage. Deep temporal models—including LSTM, TCN, and Transformer—are employed to jointly model wrist- and chest-based physiological signals. This approach substantially improves system robustness and generalization, achieving state-of-the-art performance with 98.91% accuracy and a macro F1-score of 98.56% in multimodal settings.

Abstract

Physiological stress and emotion recognition are important for health monitoring and affective computing. In this work, we present a comprehensive evaluation of deep learning models such as Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), and Transformer on the WESAD dataset for multimodal affect recognition using wrist and chest sensor signals. We perform ablation studies to assess the individual contributions of each modality by training models on wrist-only and chest-only inputs. In addition, we implement a late-fusion ensemble strategy that combines predictions from all three architectures trained on multimodal input. We also employ early fusion at the sensor level by concatenating wrist and chest signals before feeding them into each model. Our results show that Transformer models consistently achieve the highest accuracy in multimodal settings, while TCN models perform best in the wrist-only configuration. The ensemble method yields the highest overall accuracy (98.91 +/- 0.13%) and macro-F1 score (98.56 +/- 0.17%). These findings demonstrate the effectiveness of sensor fusion and ensemble-based fusion in developing robust systems for physiological emotion recognition.

Problem

• multimodal emotion recognition
• physiological signals
• sensor fusion
• affective computing
• stress recognition

Innovation

• Temporal Modeling
• Ensemble Fusion
• Multimodal Emotion Recognition
• Physiological Signals
• Sensor Fusion