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This research advances emotion recognition in wearable devices by leveraging self-supervised learning on graph neural networks to overcome data scarcity and labeling bias. By utilizing subgraph sampling with only 20–25% of available labels, the method achieves significant accuracy gains over full-resource baselines while maintaining robustness against inter-subject variability. The approach demonstrates how SSL-driven augmentation can effectively model complex emotional states like arousal and valence in real-world, unstructured environments.
Self-Supervised Graph Representation Learning for In-The-Wild Wearable and Smartphone based Emotion Recognition
arXiv:2608.22387v1 Announce Type: cross Abstract: Wearable and smartphone-based emotion recognition (WER) remains a challenging setting in affective computing, due to the notorious difficulty and bias associated with in-the-wild label collection. The high inter-and intra-subject emotional variability motivates us to explore WER modeling through graph node classification in a limited resources learning scheme powered by Self-Supervised Learning (SSL) graph masking augmentation tasks. We employ a subgraph sampling approach during training, utilizing labeled and unlabeled data, along with supervised, semi-supervised, and SSL mechanisms in a multi-task inductive graph neural network architecture. Our evaluations on K-EmoPhone through leave-one-group-out cross-validation in the binary arousal and valence tasks yield average accuracy gains of 4.3% and 7.8%, compared to the full resource setting, utilizing only 20% and 25% of the labels, respectively. Our model analysis sheds light on the relation of SSL graph augmentations to emotional arousal and valence and justifies the approach of SSL-driven subgraph training for in-the-wild WER.
Abstract:Wearable and smartphone-based emotion recognition (WER) remains a challenging setting in affective computing, due to the notorious difficulty and bias associated with in-the-wild label collection. The high inter-and intra-subject emotional variability motivates us to explore WER modeling through graph node classification in a limited resources learning scheme powered by Self-Supervised Learning (SSL) graph masking augmentation tasks. We employ a subgraph sampling approach during training, utilizing labeled and unlabeled data, along with supervised, semi-supervised, and SSL mechanisms in a multi-task inductive graph neural network architecture. Our evaluations on K-EmoPhone through leave-one-group-out cross-validation in the binary arousal and valence tasks yield average accuracy gains of 4.3% and 7.8%, compared to the full resource setting, utilizing only 20% and 25% of the labels, respectively. Our model analysis sheds light on the relation of SSL graph augmentations to emotional arousal and valence and justifies the approach of SSL-driven subgraph training for in-the-wild WER.
Published at ICASSP 2025. Copyright 2025 IEEE
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
arXiv:2608.22387 [cs.LG]
(or arXiv:2608.22387v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.22387 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1109/ICASSP49660.2025.10888648 Focus to learn more DOI(s) linking to related resources
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