Leveraging graph neural networks and gate recurrent units for accurate and transparent prediction of baseball pitching speed.

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 optimizat

SUBMITTER: Yang C 

PROVIDER: S-EPMC11882905 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature