ACL Health Screening: Automated Computer Vision System for Drop Jump Joint Angle Analysis

Document Type

Poster Presentation

Publication Date

4-17-2026

Keywords

fsc2026

Abstract

Anterior cruciate ligament (ACL) injuries remain a major concern in sport due to long rehabilitation timelines, decreased performance, and increased risk of future injury. Several biomechanical variables are commonly associated with ACL injury mechanisms, including dynamic knee valgus, knee flexion strategy during landing, and the magnitude and timing of forces at initial contact. However, traditional motion analysis methods used to assess these factors are often expensive, time-intensive, and difficult to implement in applied sport settings. This project aimed to develop a more practical and automated approach to ACL-related movement screening through a computer vision system for drop jump analysis. Using a two-camera setup integrated with force plate data, the system automatically transfers and analyzes video to identify key variables such as knee valgus, knee flexion, initial ground contact, and peak force timing. These outputs are then automatically translated into graphs and coach-facing reports with minimal manual processing. Grounded in a literature review of ACL injury mechanisms and biomechanical risk factors, this system demonstrates how automation can improve the accessibility and efficiency of movement screening in sport science environments. While not intended as a diagnostic tool, the system provides a useful foundation for athlete screening, monitoring, and future research. Ongoing work is exploring the relationship between gluteus medius activation, landing mechanics, and knee valgus, with future investigation into the role of ankle mechanics in the kinetic chain.

Comments

Poster presented at the 2026 Fisher Showcase, St. John Fisher University, April 17, 2026.

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