ArticleProceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine2025
Interpretable prediction of knee joint loading during tennis serves based on GNN-GRU model and layer-wise relevance propagation.
Article in Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Evaluating tennis player performance based on different types of grip strength.BMC sports science, medicine & rehabilitation · 2026Article
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7 authors.
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Abstract
The knee resultant joint moment is a critical indicator for assessing risk during the tennis serve. Traditional methods for obtaining this metric rely on laboratory-based equipment, limiting practical application. To address this limitation, this study proposes and validates a novel method for predicting the knee resultant joint moment method using a Graph Neural Network and Gated Recurrent Unit (GNN-GRU) model. An independent GRU model was used as a baseline for comparison. Biomechanical data were collected from 30 male tennis players (age: 20.30 ± 1.66 years, height: 176.60 ± 2.74 cm, weight: 70.80 ± 3.89 kg, BMI: 22.71 ± 1.38 kg/m
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