ArticleScientific reports2025
Leveraging graph neural networks and gate recurrent units for accurate and transparent prediction of baseball pitching speed.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Air quality prediction model based on deep learning hybrid framework.Scientific reports · 2026Article
- A data-driven analysis of spatiotemporal cues and experience accumulation effects for pitch type prediction.PloS one · 2026Article
- Study on rural landscape design strategies integrating computer vision and deep learning: an analysis based on human perception and visual aesthetics.Scientific reports · 2025Article
- Interpretable prediction of knee joint loading during tennis serves based on GNN-GRU model and layer-wise relevance propagation.Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine · 2025Article
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3 authors.
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Abstract
Long short-term memory (LSTM) networks are widely used in biomechanical data analysis but have the significant limitations in interpretability and decision transparency. Combining graph neural networks (GNN) with gate recurrent units (GRU) may offer a better solution. This study proposes and validates a hybrid GNN-GRU model for predicting baseball pitching speed and enhancing its interpretability using layer-wise relevance propagation (LRP). C3D data from 53 baseball athletes were downloaded from a public dataset. Kinematic features of 9 joints and pitching speed during the pitching process were calculated using Visual3D, resulting in a total of 208 valid pitches. The feature data were input into both LSTM and GNN-GRU hybrid models, with hyperparameters tuned using particle swarm optimization. LRP was employed to obtain the contribution rate changes of kinematic features to the prediction results throughout the pitching cycle. The prediction accuracy of the models was evaluated using mean absolute error (MAE), mean squared error (MSE), and R-squared (R
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