Muscle Forces During Single-Leg Jump Landing

Musculoskeletal modelling is widely used to estimate internal loading conditions. In order to optimise robustness and reduce errors between the subject-specific reference motion data (RMD) and the musculoskeletal simulation, 90 permutations of kinetic and kinematic data were analysed during split squats. A ranking for the scaling and kinematic weighting concepts based on the RMS errors when including functional centres of rotation (fCoRs), joint angles, and skin markers, revealed that analyses should include fCoR in the scaling and the simulation processes, as well as an automated weighting procedure including all attached skin markers for optimal registration of the musculoskeletal model to the RMD.

Journal of Biomechanics, 1998

An analytical model of the knee joint was developed to estimate the forces at the knee during exercise. Muscle forces were estimated based upon electromyographic activities during exercise and during maximum voluntary isometric contraction (MVIC), physiological cross-sectional area (PCSA), muscle fiber length at contraction and the maximum force produced by an unit PCSA under MVIC. Tibiofemoral compressive force and cruciate ligaments' tension were determined by using resultant force and torque at the knee, muscle forces, and orientations and moment arms of the muscles and ligaments. An optimization program was used to minimize the errors caused by the estimation of the muscle forces. The model was used in a ten-subject study of open kinetic chain exercise (seated knee extension) and closed kinetic chain exercises (leg press and squat). Results calculated with this model were compared to those from a previous study which did not consider muscle length and optimization. Peak tibiofemoral compressive forces were 3134$1040 N during squat, 3155$755 N during leg press and 3285$1927 N during knee extension. Peak posterior cruciate ligament tensions were 1868$878 N during squat, 1866$383 N during leg press and 959$300 N for seated knee extension. No significant anterior cruciate ligament (ACL) tension was found during leg press and squat. Peak ACL tension was 142$257 N during seated knee extension. It is demonstrated that the current model provided better estimation of knee forces during exercises, by preventing significant overestimates of tibiofemoral compressive forces and cruciate ligament tensions.

Journal of Biomechanical Engineering, 2003

The purpose of this study was to develop a subject-specific 3-D model of the lower extremity to predict neuromuscular control effects on 3-D knee joint loading during movements that can potentially cause injury to the anterior cruciate ligament (ACL) in the knee. The simulation consisted of a forward dynamic 3-D musculoskeletal model of the lower extremity, scaled to represent a specific subject. Inputs of the model were the initial position and velocity of the skeletal elements, and the muscle stimulation patterns. Outputs of the model were movement and ground reaction forces, as well as resultant 3-D forces and moments acting across the knee joint. An optimization method was established to find muscle stimulation patterns that best reproduced the subject's movement and ground reaction forces during a sidestepping task. The optimized model produced movements and forces that were generally within one standard deviation of the measured subject data. Resultant knee joint loading variables extracted from the optimized model were comparable to those reported in the literature. The ability of the model to successfully predict the subject's response to altered initial conditions was quantified and found acceptable for use of the model to investigate the effect of altered neuromuscular control on knee joint loading during sidestepping. Monte Carlo simulations (Nϭ100,000) using randomly perturbed initial kinematic conditions, based on the subject's variability, resulted in peak anterior force, valgus torque and internal torque values of 378 N, 94 Nm and 71 Nm, respectively, large enough to cause ACL rupture. We conclude that the procedures described in this paper were successful in creating valid simulations of normal movement, and in simulating injuries that are caused by perturbed neuromuscular control.

Journal of Biomechanics, 2014

The aim of this paper was to compare the effect of different optimization methods and different knee joint degrees of freedom (DOF) on muscle force predictions during a single legged hop. Nineteen subjects performed single-legged hopping manoeuvres and subjectspecific musculoskeletal models were developed to predict muscle forces during the movement. Muscle forces were predicted using static optimization (SO) and computed muscle control (CMC) methods using either 1 or 3 DOF knee joint models. All sagittal and transverse plane joint angles calculated using inverse kinematics or CMC in a 1 DOF or 3 DOF knee were well-matched (RMS error < 3 o). Biarticular muscles (hamstrings, rectus femoris and gastrocnemius) showed more differences in muscle force profiles when comparing between the different muscle prediction approaches where these muscles showed larger time delays for many of the comparisons. The muscle force magnitudes of vasti, gluteus maximus and gluteus medius were not greatly influenced by the choice of muscle force prediction method with low normalized root mean squared errors (< 48%) observed in most comparisons. We conclude that SO and CMC can be used to predict lower-limb muscle co-contraction during hopping movements. However, care must be taken in interpreting the magnitude of force predicted in the biarticular muscles and the soleus, especially when using a 1 DOF knee. Despite this limitation, given that SO is a more robust and computationally efficient method for predicting muscle forces than CMC, we suggest that SO can be used in conjunction with musculoskeletal models that have a 1 or 3 DOF knee joint to study the relative differences and the role of muscles during hopping activities in future studies.

In this study we used musculoskeletal modelling with mathematical optimization tools to find whole-body kinematics that simultaneously reduce risk of injury and enhance sports performance. Combining these objectives has long been the goal of sports science research. We focused on improving hang-time parameters in volleyball (Gupta et al., 2015). We were able to preserve an advantage of hang-time (late swing) and address its disadvantage (potential loss in peak height of the hitting arm) by increasing the height of the hitting wrist by 1 cm, while at the same time not increasing the shoulder moments. This study provided a proof of concept that this optimization framework can potentially find a balance between performance and injury prevention in a complex sports task. INTRODUCTION: Finding the whole body kinematic pattern that enhances performance with minimal risk of injury has long been the goal of research in biomechanics in sports. The human body is a multi-segment, multi-degree of freedom " machine " with complex connections between segments. Hence, both overall performance quality and injury in one segment could be due to the movement of a completely different segment, a segment that might not even be directly connected to the performing or injured segment. The majority of previous studies have focused on movement of one segment or the action of musculature around that segment to address the issues of performance and/or injury prevention (Reeser et al., 2010; Seminati et al., 2013). Although these studies provide great insights, they provide incomplete causal information about the complex multi-segmental dynamics of movement tasks. This information requires study of the full body during the task. In-silico simulations in conjunction with optimization methods have been used to identify whole-body kinematics for reducing peak valgus knee moments for a side-stepping task (Donnelly et al., 2012) during the weight acceptance phase to prevent ACL injury. They used the open source musculoskeletal modelling software OpenSim (an open source software available at the website simtk.org.) to produce in-silico simulation of the movement pattern based on motion data. Residual reduction algorithm (RRA) is an optimization tool within OpenSim capable of altering the whole-body kinematics. This tool can be used through an outer level optimization process (Reinbolt et al., 2011) to find a new movement pattern that reduces peak knee valgus moments and makes the simulations run with negligible residual forces and moments. The outer level optimization (Reinbolt et al., 2011) essentially works based on the definition of cost function that encapsulates the aims of the optimization process. Donnelly et al. (2012) used it to loosely follow the original movement pattern, reduce the residuals to near 0 and reduce the peak knee valgus moments. Since RRA within OpenSim allows for calculation of the whole-body kinematics and the corresponding joint torques, the outer level cost function can be reprogramed such that it tries to enhance performance parameters and reduce injury risk factors like high joint torques.

Subject-specific musculoskeletal models have become key tools in the clinical decision-making process. However, the sensitivity of the calculated solution to the unavoidable errors committed while deriving the model parameters from the available information is not fully understood. The aim of this study was to calculate the sensitivity of all the kinematics and kinetics variables to the inter-examiner uncertainty in the identification of the lower limb joint models. The study was based on the computer tomography of the entire lower-limb from a single donor and the motion capture from a body-matched volunteer. The hip, the knee and the ankle joint models were defined following the International Society of Biomechanics recommendations. Using a software interface, five expert anatomists identified on the donor’s images the necessary bony locations five times with a three-day time interval. A detailed subject-specific musculoskeletal model was taken from an earlier study, and re-formulated to define the joint axes by inputting the necessary bony locations. Gait simulations were run using OpenSim within a Monte Carlo stochastic scheme, where the locations of the bony landmarks were varied randomly according to the estimated distributions. Trends for the joint angles, moments, and the muscle and joint forces did not substantially change after parameter perturbations. The highest variations were as follows: (a) 118 calculated for the hip rotation angle, (b) 1% BW x H calculated for the knee moment and (c) 0.33 BW calculated for the ankle plantarflexor muscles and the ankle joint forces. In conclusion, the identification of the joint axes from clinical images is a robust procedure for human movement modelling and simulation.

Journal of Biomechanics, 2007

Although a number of approaches have attempted to model knee kinematics, rarely have they been validated against in vivo data in a larger subject cohort. Here, we assess the feasibility of four-bar linkage mechanisms in addressing knee kinematics and propose a new approach that is capable of accounting for lengthening characteristics of the ligaments, including possible laxity, as well as the internal/ external rotation of the joint.

Gait & posture, 2010

2006

Foundations for the design of a human biomechanical model aimed at analyzing planar movements such as vertical or standing long jumps are presented. The motions in the hip, knee and ankle joints are modeled as enforced by muscle forces applied to the tendon attachment points, and the other joint motions are actuated by torques representing the muscle action. A systematic construction of the related dynamic equations in independent coordinates is developed, enhanced by an effective scheme for determination of reaction forces in the leg joints. The reaction forces from the ground during the support phase can also be obtained.

Journal of Biomechanics

Compressive forces experienced at the knee can significantly contribute to cartilage degeneration. Musculoskeletal models enable predictions of the internal forces experienced at the knee, but validation is often not possible, as experimental data detailing loading at the knee joint is limited. Recently available data reporting compressive knee force through direct measurement using instrumented total knee replacements offer a unique opportunity to evaluate the accuracy of models. Previous studies have highlighted the importance of subject-specificity in increasing the accuracy of model predictions; however, these techniques may be unrealistic outside of a research setting. Therefore, the goal of our work was to identify a practical approach for accurate prediction of tibiofemoral knee contact force (KCF). Four methods for prediction of knee contact force were compared: (1) standard static optimization, (2) uniform muscle coordination weighting, (3) subject-specific muscle coordinat...