棒球投掷中可扩展的伤害风险筛查
Scalable Injury-Risk Screening in Baseball Pitching From Broadcast Video
AI总结：
基于单目视频的棒球投掷生物力学指标提取，实现可扩展的伤害风险筛查。
摘要：
在投掷运动中预测伤害取决于精确的生物力学信号，但标准测量来自昂贵的多摄像头系统，这些系统只能在专业场地使用。我们提出了一种单目视频管道，能够从直播视频中恢复18个临床相关生物力学指标，将姿态衍生的运动学作为可扩展的伤害风险建模来源。基于DreamPose3D，我们的方法引入了漂移控制的全局提升模块，通过基于速度的参数化和滑动窗口推断恢复骨盆轨迹，将骨盆根部的姿态提升到全局空间。为了解决运动模糊、压缩伪影和极端投掷姿态，我们结合了具有骨长约束、关节限逆运动学、平滑和对称约束的运动学细化流程，以确保时间稳定且物理上合理的运动学。在13名专业投手（156对投掷）上，16/18个指标达到亚度级一致性（MAE<1°）。使用这些指标进行伤害预测，自动化筛查模型在7348名投手中实现了Tommy John手术的AUC 0.811和显著臂伤的AUC 0.825。由此得到的姿态衍生指标支持可扩展的伤害风险筛查，确立了单目直播视频作为专业场地运动捕捉的可行替代方案。
英文摘要：
Injury prediction in pitching depends on precise biomechanical signals, yet gold-standard measurements come from expensive, stadium-installed multi-camera systems that are unavailable outside professional venues. We present a monocular video pipeline that recovers 18 clinically relevant biomechanics metrics from broadcast footage, positioning pose-derived kinematics as a scalable source for injury-risk modeling. Built on DreamPose3D, our approach introduces a drift-controlled global lifting module that recovers pelvis trajectory via velocity-based parameterization and sliding-window inference, lifting pelvis-rooted poses into global space. To address motion blur, compression artifacts, and extreme pitching poses, we incorporate a kinematics refinement pipeline with bone-length constraints, joint-limited inverse kinematics, smoothing, and symmetry constraints to ensure temporally stable and physically plausible kinematics. On 13 professional pitchers (156 paired pitches), 16/18 metrics achieve sub-degree agreement (MAE $< 1^{\circ}$). Using these metrics for injury prediction, an automated screening model achieves AUC 0.811 for Tommy John surgery and 0.825 for significant arm injuries on 7,348 pitchers. The resulting pose-derived metrics support scalable injury-risk screening, establishing monocular broadcast video as a viable alternative to stadium-scale motion capture for biomechanics.
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