ArticleFrontiers in sports and active living2025
Development of a MediaPipe-based framework for biomechanical quantification of table tennis forehand strokes.
Article in Frontiers in sports and active living, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A multimodal deep learning-based model for posture asymmetry recognition and sports injury risk prediction in adolescent table tennis athletes.Frontiers in physiology · 2026Article
- Multi-objective optimization of table tennis serve kinematic parameters using an improved particle swarm optimization algorithm.Frontiers in physiology · 2026Article
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Authors and funding
4 authors.
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Abstract
Introduction: This study aimed to quantify kinematic relationships across body segments during forehand strokes to provide interpretable metrics for single-camera based lightweight table tennis diagnostics. Methods: We analyzed 34 female players (aged 9.1-21.7 years) from provincial teams, recording a total of 340 strokes (10 per player). An SVM model was used to predict ball speed, after which 320 strokes (8-10 per player) were retained by removing outliers in ball speed. From MediaPipe position time series, we calculated velocity, angle and angular velocity time series, and extracted kinematic parameters from these time series, including range, mean/peak/impact values. Within-subject correlation coefficients ( Results: Ball speed increased with greater playing-side arm linear movement at the shoulder ( Discussion: This MediaPipe-based framework demonstrates potential for efficient biomechanical analysis in table tennis, providing a promising foundation for lightweight real-time analysis solutions.
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