Article: Generative adversarial network-enhanced spatio-temporal graph convolution for driving fatigue monitoring in athletic training Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.28 pp.25 - 42 Abstract: Fatigue monitoring is essential for athletic training. This study addresses class imbalance and scarcity of severe fatigue samples by proposing a spatio-temporal graph convolutional network enhanced with a generative adversarial network. A conditional Wasserstein generative adversarial network generates realistic synthetic skeletal sequences to expand the training set. Combined with spatio-temporal feature extraction, our model achieves end-to-end fatigue level classification. Evaluations on the daily life activities dataset demonstrate superior performance, with accuracy, precision, recall, and F1-score reaching 92.5%, 91.8%, 92.2%, and 92.0% respectively - outperforming support vector machine by 20.2%, long short-term memory by 11.0%, baseline spatio-temporal graph convolutional network by 3.8%, and variational autoencoder-augmented models by 2.8% in accuracy. Ablation studies validate both the generative adversarial network augmentation and nonlinear labelling strategy, offering a reliable vision-based framework for fatigue monitoring. Inderscience Publishers - linking academia, business and industry through research

Title: Generative adversarial network-enhanced spatio-temporal graph convolution for driving fatigue monitoring in athletic training

Authors: Hua Zhao

Addresses: Department of Police Physical Training, Shanxi Police College, Taiyuan, 030401, China

Abstract: Fatigue monitoring is essential for athletic training. This study addresses class imbalance and scarcity of severe fatigue samples by proposing a spatio-temporal graph convolutional network enhanced with a generative adversarial network. A conditional Wasserstein generative adversarial network generates realistic synthetic skeletal sequences to expand the training set. Combined with spatio-temporal feature extraction, our model achieves end-to-end fatigue level classification. Evaluations on the daily life activities dataset demonstrate superior performance, with accuracy, precision, recall, and F1-score reaching 92.5%, 91.8%, 92.2%, and 92.0% respectively - outperforming support vector machine by 20.2%, long short-term memory by 11.0%, baseline spatio-temporal graph convolutional network by 3.8%, and variational autoencoder-augmented models by 2.8% in accuracy. Ablation studies validate both the generative adversarial network augmentation and nonlinear labelling strategy, offering a reliable vision-based framework for fatigue monitoring.

Keywords: generative adversarial networks; GANs; spatio-temporal graph convolution; exercise fatigue monitoring; data enhancement; Wasserstein generative adversarial networks.

DOI: 10.1504/IJICT.2026.152549

International Journal of Information and Communication Technology, 2026 Vol.27 No.28, pp.25 - 42

Received: 07 Sep 2025
Accepted: 22 Oct 2025
Published online: 26 Mar 2026 *

Title: Generative adversarial network-enhanced spatio-temporal graph convolution for driving fatigue monitoring in athletic training

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