Deep Temporal Modeling and Ensemble Fusion for Multimodal Emotion Recognition from Physiological Signals
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 leve...
| Authors | Desta Haileselassie Hagos, Saurav Keshari Aryal, Patrick Ymele-Leki et al. |
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| Primary category | cs.CL |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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