VRBT: VR Badminton Training with Multitask Injury Alerts based on Lightweight 3D Skeletal Reconstruction

doi:10.16182/j.issn1004731x.joss.25-0862

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.

Key words: virtual reality(VR), badminton training, 3D skeleton recognition, sports injury early warning, multi-task learning

CLC Number:

TP391.9

Zhu Yuning, Yang Meng, Chen Tianyue, Meng Weiliang. VRBT: VR Badminton Training with Multitask Injury Alerts based on Lightweight 3D Skeletal Reconstruction[J]. Journal of System Simulation, 2026, 38(1): 225-234.

Figures/Tables 6

Fig. 1

Table 1

Force exerted by a badminton ball in the air

力的类型	方向	计算公式	参数说明
重力	竖直向下	$F g = m ⋅ g$	m：球质量 g：重力加速度
空气阻力	与运动方向相反	$F d = 12 ρ v 2 C d A$	ρ：空气密度 v：瞬时速度 C_d：阻力系数 A：迎风面积
马格努斯力	垂直于运动与旋转轴	$F m = 12 ρ v 2 C L A$	C_L：升力系数依赖旋转角速度，旋转方向决定力的指向
浮力	竖直向上	$F b = ρ a i r V g$	V：羽毛球体积
侧风力	水平方向	$F w = 12 ρ v w 2 C w A$	v_w：风速 C_w：侧向阻力系数
粘性阻力	与运动方向相反	$F v = C v ⋅ A ⋅ μ ⋅ v L$	C_v：形状相关常数 A：物体表面积 L：特征长度

Table 1

Fig. 2

Fig. 3

Table 2

Table 3

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主要参数	量值
损伤概率判别能力 (AUC)	0.953 ± 0.012
风险等级分类准确率(Accuracy)	0.945 ± 0.015
预警类型识别性能 (Micro-F1 Score)	0.935 ± 0.018

模型	AUC	Accuracy	Micro-F1 Score
XGBoost	0.530	0.214	0.961
Random Forest	0.897	0.723	0.703
GBDT	0.929	0.836	0.824
DART&GBDT	0.932	0.834	0.820
本文模型	0.953	0.945	0.935