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

Personalized neuromusculoskeletal (NMS) models can represent the neurological, physiological, and anatomical characteristics of an individual and can be used to estimate the forces generated inside the human body. Currently, publicly available software to calculate muscle forces are restricted to static and dynamic optimisation methods, or limited to isometric tasks only. We have created and made freely available for the research community the Calibrated EMG-Informed NMS Modelling Toolbox (CEINMS), an OpenSim plug-in that enables investigators to predict different neural control solutions for the same musculoskeletal geometry and measured movements. CEINMS comprises EMG-driven and EMG-informed algorithms that have been previously published and tested. It operates on dynamic skeletal models possessing any number of degrees of freedom and musculotendon units and can be calibrated to the individual to predict measured joint moments and EMG patterns. In this paper we describe the components of CEINMS and its integration with OpenSim. We then analyse how EMG-driven, EMG-assisted, and static optimisation neural control solutions affect the estimated joint moments, muscle forces, and muscle excitations, including muscle co-contraction.

Excessive contact force is believed to contribute to the development of medial compartment knee osteoarthritis. The external knee adduction moment (KAM) has been identified as a surrogate measure for medial contact force during gait, with an abnormally large peak value being linked to increased pain and rate of disease progression. This study used in vivo gait data collected from a subject with a force-measuring knee implant to assess whether KAM decreases accurately predict corresponding decreases in medial contact force. Changes in both quantities generated via gait modification were analyzed statistically relative to the subject's normal gait. The two gait modifications were a ''medial thrust'' gait involving knee medialization during stance phase and a ''walking pole'' gait involving use of bilateral walking poles. Reductions in the first (largest) peak of the KAM (32-33%) did not correspond to reductions in the first peak of the medial contact force. In contrast, reductions in the second peak and angular impulse of the KAM (15-47%) corresponded to reductions in the second peak and impulse of the medial contact force (12-42%). Calculated reductions in both KAM peaks were highly sensitive to rotation of the shank reference frame about the superior-inferior axis of the shank. Both peaks of medial contact force were best predicted by a combination of peak values of the external KAM and peak absolute values of the external knee flexion moment (R 2 ¼ 0.93). Future studies that evaluate the effectiveness of gait modifications for offloading the medial compartment of the knee should consider the combined effect of these two knee moments. ß

Estimation of muscle forces during motion involves solving an indeterminate problem (more unknown muscle forces than joint moment constraints), frequently via optimization methods. When the dynamics of muscle activation and contraction are modeled for consistency with muscle physiology, the resulting optimization problem is dynamic and challenging to solve. This study sought to identify a robust and computationally efficient formulation for solving these dynamic optimization problems using direct collocation optimal control methods. Four problem formulations were investigated for walking based on both a two and three dimensional model. Formulations differed in the use of either an explicit or implicit representation of contraction dynamics with either muscle length or tendon force as a state variable. The implicit representations introduced additional controls defined as the time derivatives of the states, allowing the nonlinear equations describing contraction dynamics to be imposed as algebraic path constraints, simplifying their evaluation. Problem formulation affected computational speed and robustness to the initial guess. The formulation that used explicit contraction dynamics with muscle length as a state failed to converge in most cases. In contrast, the two formulations that used implicit contraction dynamics converged to an optimal solution in all cases for all initial guesses, with tendon force as a state generally being the fastest. Future work should focus on comparing the present approach to other approaches for computing muscle forces. The present approach lacks some of the major limitations of established methods such as static optimization and computed muscle control while remaining computationally efficient.Electronic Supplementary MaterialThe online version of this article (doi:10.1007/s10439-016-1591-9 contains supplementary material, which is available to authorized users.