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Self-Supervised Graph Representation Learning for In-The-Wild Wearable and Smartphone based Emotion Recognition

2026-08-23 12:15:52
Ioannis N. Ziogas, Leontios J. Hadjileontiadis, Ahsan H. Khandoker, Aamna Al Shehhi
arXiv_AI
             
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 (translated)

URL

https://arxiv.org/abs/2608.22387

PDF

https://arxiv.org/pdf/2608.22387.pdf

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