1423 - Markerless Motion Capture Assessment of Dynamic Knee Valgus during Marathon Training in Recreational Runners

Increased dynamic knee valgus can predispose athletes to knee injuries including patellofemoral pain or non-contact ACL injuries. Studies using marker-based motion capture or video analysis to evaluate knee valgus during single leg squat (SLS) demonstrated that females have greater knee valgus, reduced knee flexion components of dynamic valgus may help further predict injury, and greater hip abductor strength was correlated with less valgus. Markerless motion capture use has become easier, increasingly accurate, and validated for assessing kinematic movements. Markerless motion capture has not yet been used to assess dynamic knee valgus in runners or to quantify changes in knee control with training.

This study aims to determine if a 12-camera markerless motion capture system can accurately reconstruct kinematics during functional movement screening to analyze dynamic knee valgus during SLS. The secondary aim is to compare knee valgus kinematics before and after a cross-training program is implemented in recreational marathon runners.

Design: Prospective cohort study

Results: It is feasible to use markerless technology to assess kinematics during functional movement screening of 14 people in one morning prior to marathon training. Overlays of the biomechanical reconstructions confirmed validity, and participants showed a range of dynamic valgus during SLS at baseline assessment. Knee valgus kinematics will again be collected in 2 weeks, 20 weeks after the cross-training program initiation. Secondary aim data is pending.  

Conclusions: Markerless motion capture data can quantify knee valgus during SLS. Identifying athletes with increased dynamic knee valgus allows for targeted neuromuscular control and strengthing interventions to potentially lower injury risk in runners during marathon training. Markerless motion capture lowers barriers to movement analysis, demonstrated by quantifying functional movement screens from 14 people in one morning. This technology shows great promise for more accessible biomechanical analysis of clinical and athlete populations leading to future personalized training and rehabilitation interventions.