A patella marker to improve hip and knee kinematics for models with functionally defined joint axes

Background: The clinical utility of motion capture modeling relies on the accurate tracking of segment motions. Soft tissue artefact presents a particular challenge for modeling hip rotation, knee rotation, and knee varus-valgus motions. The integration of a patella marker has been shown to significantly improve hip rotation tracking for models that utilize anatomical definitions of joint axes (e.g. anatomical models). However, these modeling improvements have not been extended to models that use functional segment motion to define joint axes (e.g. functional models). Research question: How does the positioning of a patella marker influence functional model performance? Methods: A patella functional model (PFM) was created by integrating a patella marker into the functional model (FM) used at our center. Nine distinct versions of the PFM were created using a 3 × 3 grid of markers placed across the patella. Ten typically developing participants performed controlled hip rotation, controlled knee flexion-extension, and free speed walking trials to assess FM and PFM performance differences. Results: The top performing PFM modeled 98 ± 8 % of the reference hip rotation range of motion compared to 71 ± 9 % for the FM. This PFM had low sensitivity to knee flexion-extension motion, 5 ± 10 %. For walking kinematics, this top performing PFM reported 14 % greater hip rotation ROM during stance, 46 % less knee rotation ROM over the entire gait cycle, and 32 % less knee varus-valgus during swing compared to the FM. The differences in modeling are nearly identical to those reported between skin mounted marker and fluoroscopy-based models, indicating that utilization of the patella marker leads to improvements in tracking accuracy. Significance: Utilization of a precisely placed patella marker led to substantial improvements in modeled hip rotation, knee rotation, and knee varus-valgus. These improvements have the potential to positively impact those specialties that rely on motion capture modeling for clinical decision-making, such as orthopedic surgery.