Impairment of the human neuromusculoskeletal system can lead to significant mobility limitations and decreased quality of life. Computational models that accurately represent the musculoskeletal systems of individual patients could be used to explore different treatment options and ultimately to optimize clinical outcome. The most significant barrier to model-based treatment design is validation of model-based estimates of in vivo contact and muscle forces. This paper introduces an annual “Grand Challenge Competition to Predict In Vivo Knee Loads” based on a series of comprehensive publicly available in vivo data sets for evaluating musculoskeletal model predictions of contact and muscle forces in the knee. The data sets come from patients implanted with force-measuring tibial prostheses. Following a historical review of musculoskeletal modeling methods used for estimating knee muscle and contact forces, we describe the first two data sets used for the first two competitions and summarize four subsequent data sets to be used for future competitions. These data sets include tibial contact force, video motion, ground reaction, muscle EMG, muscle strength, static and dynamic imaging, and implant geometry data. Competition participants create musculoskeletal models to predict tibial contact forces without having access to the corresponding in vivo measurements, which are not released until after each year’s competition submissions. These blinded predictions provide an unbiased evaluation of the capabilities and limitations of musculoskeletal modeling methods. The paper concludes with a discussion of how these unique data sets can be used by the musculoskeletal modeling research community to improve the estimation of in vivo muscle and contact forces and ultimately to help make musculoskeletal models clinically useful.

ABSTRACT:The external knee adduction torque has been proposed as a surrogate measure for medial compartment load during gait. However, a direct link between these two quantities has not been demonstrated using in vivo measurement of medial compartment load. This study uses in vivo data collected from a single subject with an instrumented knee implant to evaluate this link. The subject performed five different overground gait motions (normal, fast, slow, wide, and toe-out) with simultaneous collection of instrumented implant, video motion, and ground reaction data. For each trial, the knee adduction torque was measured externally while the total axial force applied to the tibial insert was measured internally. Based on data collected from the same subject performing treadmill gait under fluoroscopic motion analysis, a regression equation was developed to calculate medial contact force from the implant load cell measurements. Correlation analyses were performed for the stance phase and entire gait cycle to quantify the relationship between the knee adduction torque and both the medial contact force and the medial to total contact force ratio. When the entire gait cycle was analyzed, R 2 for medial contact force was 0.77 when all gait trials were analyzed together and between 0.69 and 0.93 when each gait trial was analyzed separately (p < 0.001 in all cases). For medial to total force ratio, R 2 was 0.69 for all trials together and between 0.54 and 0.90 for each trial separately (p < 0.001 in all cases). When only the stance phase was analyzed, R 2 values were slightly lower. These results support the hypothesis that the knee adduction torque is highly correlated with medial compartment contact force and medial to total force ratio during gait. ß

Estimating tibiofemoral joint contact forces is important for understanding the initiation and progression of knee osteoarthritis. However, tibiofemoral contact force predictions are influenced by many factors including muscle forces and anatomical representations of the knee joint. This study aimed to investigate the influence of subject-specific geometry and knee joint kinematics on the prediction of tibiofemoral contact forces using a calibrated EMG-driven neuromusculoskeletal model of the knee. One participant fitted with an instrumented total knee replacement walked at a self-selected speed while medial and lateral tibiofemoral contact forces, ground reaction forces, whole-body kinematics, and lower-limb muscle activity were simultaneously measured. The combination of generic and subject-specific knee joint geometry and kinematics resulted in four different OpenSim models used to estimate muscle-tendon lengths and moment arms. The subject-specific geometric model was created from CT scans and the subject-specific knee joint kinematics representing the translation of the tibia relative to the femur was obtained from fluoroscopy. The EMG-driven model was calibrated using one walking trial, but with three different cost functions that tracked the knee flexion/extension moments with and without constraint over the estimated joint contact forces. The calibrated models then predicted the medial and lateral tibiofemoral contact forces for five other different walking trials. The use of subject-specific models with minimization of the peak tibiofemoral contact forces improved the accuracy of medial contact forces by 47% and lateral contact forces by 7%, respectively compared with the use of generic musculoskeletal model.

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Progression of stifle osteoarthritis in dogs treated with TPLO may be partly the result of abnormal stifle contact mechanics induced by altering the orientation of the proximal tibial articulating surface.