Injury prevention in sports requires understanding how bio-mechanical risks emerge from movement patterns captured in real-world scenarios. However, identifying and interpreting injury prone events from raw video remains difficult and time-consuming. We present VAIR, a visual analytics system that supports injury risk analysis using 3D human motion reconstructed from sports video. VAIR combines pose estimation, bio-mechanical simulation, and synchronized visualizations to help users explore how joint-level risk indicators evolve over time. Domain experts can inspect movement segments through temporally aligned joint angles, angular velocity, and internal forces to detect patterns associated with known injury mechanisms. Through case studies involving Achilles tendon and Anterior cruciate ligament (ACL) injuries in basketball, we show that VAIR enables more efficient identification and interpretation of risky movements. Expert feedback confirms that VAIR improves diagnostic reasoning and supports both retrospective analysis and proactive intervention planning.

翻译：运动损伤预防需要理解生物力学风险如何从现实场景中捕捉的运动模式中产生。然而，从原始视频中识别和解读易损伤事件仍然困难且耗时。我们提出了VAIR，一个利用从运动视频重建的3D人体运动来支持损伤风险分析的可视化分析系统。VAIR结合姿态估计、生物力学仿真和同步可视化，帮助用户探索关节层面的风险指标如何随时间演变。领域专家可通过时间对齐的关节角度、角速度和内力来检查运动片段，以检测与已知损伤机制相关的模式。通过涉及篮球运动中跟腱损伤和前交叉韧带（ACL）损伤的案例研究，我们表明VAIR能够更高效地识别和解读高风险运动。专家反馈证实，VAIR改善了诊断推理，并同时支持回顾性分析和前瞻性干预规划。