VAIR: Visual Analytics for Injury Risk Exploration in Sports

TL;DR

VAIR is a visual analytics system that leverages 3D motion reconstruction, biomechanical simulation, and synchronized visualization to help sports professionals identify injury risks from video data more efficiently.

Contribution

The paper introduces VAIR, a novel system combining pose estimation, biomechanical simulation, and visualization for injury risk analysis from sports videos.

Findings

VAIR improves efficiency in identifying injury-prone movements.

Expert feedback indicates enhanced diagnostic reasoning.

Case studies demonstrate VAIR's effectiveness in real injury scenarios.

Abstract

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.