Journal of System Simulation
Abstract
Abstract: To overcome the limitations of traditional badminton training, a VR training method that integrates multiple models for collaborative simulation is proposed. A "perception-decision- interaction" framework is developed within Unity, featuring diverse training modules powered by a physics engine for realistic trajectory simulation. The system employs a lightweight MHFormer for 3D pose estimation and a novel multi-task model (enhanced injury prediction system, EIPS) that combines random forest and XGBoost to jointly assess injury risk. This approach offers a solution for balancing real-time performance with accuracy in skeleton reconstruction and enables personalized training through dynamic risk assessment.
Recommended Citation
Zhu, Yuning; Yang, Meng; Chen, Tianyue; and Meng, Weiliang
(2026)
"VRBT: VR Badminton Training with Multitask Injury Alerts based on Lightweight 3D Skeletal Reconstruction,"
Journal of System Simulation: Vol. 38:
Iss.
1, Article 17.
DOI: 10.16182/j.issn1004731x.joss.25-0862
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol38/iss1/17
First Page
225
Last Page
234
CLC
TP391.9
Recommended Citation
Zhu Yuning, Yang Meng, Chen Tianyue, et al. VRBT: VR Badminton Training with Multitask Injury Alerts based on Lightweight 3D Skeletal Reconstruction[J]. Journal of System Simulation, 2026, 38(1): 225-234.
DOI
10.16182/j.issn1004731x.joss.25-0862
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