In Vivo Knee Contact Force Prediction Using Patient Specific Musculoskeletal Geometry in a Segment Based Computational Model

Abstract

Segmentbased musculoskeletal models allow the prediction of muscle, ligament, and joint forces without making assumptions regarding joint degreesoffreedom (DOF). The dataset published for the “Grand Challenge Competition to Predict in vivo Knee Loadsâ€‌ provides directly measured tibiofemoral contact forces for activities of daily living (ADL). For the Sixth Grand Challenge Competition to Predict in vivo Knee Loads, blinded results for “smoothâ€‌ and “bouncyâ€‌ gait trials were predicted using a customized patientspecific musculoskeletal model. For an unblinded comparison, the following modifications were made to improve the predictions: further customizations, including modifications to the knee center of rotation; reductions to the maximum allowable muscle forces to represent known loss of strength in knee arthroplasty patients; and a kinematic constraint to the hip joint to address the sensitivity of the segmentbased approach to motion tracking artifact. For validation, the improved model was applied to normal gait, squat, and sittostand for three subjects. Comparisons of the predictions with measured contact forces showed that segmentbased musculoskeletal models using patientspecific input data can estimate tibiofemoral contact forces with root mean square errors (RMSEs) of 0.48–0.65 times body weight (BW) for normal gait trials. Comparisons between measured and predicted tibiofemoral contact forces yielded an average coefficient of determination of 0.81 and RMSEs of 0.46–1.01 times BW for squatting and 0.70–0.99 times BW for sittostand tasks. This is comparable to the best validations in the literature using alternative models.