Deep Temporal Modeling and Ensemble Fusion for Multimodal Emotion Recognition from Physiological Signals
This study evaluates LSTM, TCN, and Transformer models on the WESAD dataset for multimodal emotion recognition using wrist and chest sensors. Transformer achieved highest accuracy in multimodal settings, TCN best on wrist-only. Ensemble late-fusion achieved 98.91% accuracy and 98.56% macro-F1.
[2606.15026] Deep Temporal Modeling and Ensemble Fusion for Multimodal Emotion Recognition from Physiological Signals
[Submitted on 12 Jun 2026]
Title:Deep Temporal Modeling and Ensemble Fusion for Multimodal Emotion Recognition from Physiological Signals
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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.
Comments: Accepted for publication in the 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (ACM BCB 2026). DOI: this https URL
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2606.15026 [cs.CL]
(or arXiv:2606.15026v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.15026
arXiv-issued DOI via DataCite (pending registration)
Related DOI:
https://doi.org/10.1145/3807503.3819363
DOI(s) linking to related resources
Submission history
From: Desta Haileselassie Hagos [view email] [v1] Fri, 12 Jun 2026 23:50:33 UTC (101 KB)
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