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      Estimation of Ligament Loading and Anterior Tibial Translation in Healthy and ACL-Deficient Knees During Gait and the Influence of Increasing Tibial Slope Using EMG-Driven Approach

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          Abstract

          The purpose of this study was to develop a biomechanical model to estimate anterior tibial translation (ATT), anterior shear forces, and ligament loading in the healthy and anterior cruciate ligament (ACL)-deficient knee joint during gait. This model used electromyography (EMG), joint position, and force plate data as inputs to calculate ligament loading during stance phase. First, an EMG-driven model was used to calculate forces for the major muscles crossing the knee joint. The calculated muscle forces were used as inputs to a knee model that incorporated a knee–ligament model in order to solve for ATT and ligament forces. The model took advantage of using EMGs as inputs, and could account for the abnormal muscle activation patterns of ACL-deficient gait. We validated our model by comparing the calculated results with previous in vitro, in vivo, and numerical studies of healthy and ACL-deficient knees, and this gave us confidence on the accuracy of our model calculations. Our model predicted that ATT increased throughout stance phase for the ACL-deficient knee compared with the healthy knee. The medial collateral ligament functioned as the main passive restraint to anterior shear force in the ACL-deficient knee. Although strong co-contraction of knee flexors was found to help restrain ATT in the ACL-deficient knee, it did not counteract the effect of ACL rupture. Posterior inclination angle of the tibial plateau was found to be a crucial parameter in determining knee mechanics, and increasing the tibial slope inclination in our model would increase the resulting ATT and ligament forces in both healthy and ACL-deficient knees.

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          Most cited references43

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          An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo.

          This paper examined if an electromyography (EMG) driven musculoskeletal model of the human knee could be used to predict knee moments, calculated using inverse dynamics, across a varied range of dynamic contractile conditions. Muscle-tendon lengths and moment arms of 13 muscles crossing the knee joint were determined from joint kinematics using a three-dimensional anatomical model of the lower limb. Muscle activation was determined using a second-order discrete non-linear model using rectified and low-pass filtered EMG as input. A modified Hill-type muscle model was used to calculate individual muscle forces using activation and muscle tendon lengths as inputs. The model was calibrated to six individuals by altering a set of physiologically based parameters using mathematical optimisation to match the net flexion/extension (FE) muscle moment with those measured by inverse dynamics. The model was calibrated for each subject using 5 different tasks, including passive and active FE in an isokinetic dynamometer, running, and cutting manoeuvres recorded using three-dimensional motion analysis. Once calibrated, the model was used to predict the FE moments, estimated via inverse dynamics, from over 200 isokinetic dynamometer, running and sidestepping tasks. The inverse dynamics joint moments were predicted with an average R(2) of 0.91 and mean residual error of approximately 12 Nm. A re-calibration of only the EMG-to-activation parameters revealed FE moments prediction across weeks of similar accuracy. Changing the muscle model to one that is more physiologically correct produced better predictions. The modelling method presented represents a good way to estimate in vivo muscle forces during movement tasks.
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            Effects of increasing tibial slope on the biomechanics of the knee.

            To determine the effects of increasing anterior-posterior (A-P) tibial slope on knee kinematics and in situ forces in the cruciate ligaments. Ten cadaveric knees were studied using a robotic testing system using three loading conditions: (1) 200 N axial compression; (2) 134 N A-P tibial load; and (3) combined 200 N axial and 134 N A-P loads. Resulting knee kinematics were determined before and after a 5-mm anterior opening wedge osteotomy. Resulting in situ forces in each cruciate ligament were determined. Tibial slope was increased from 8.8 +/- 1.8 degrees to 13.2 +/- 2.1 degrees, causing an anterior shift in the resting position of the tibia relative to the femur up to 3.6 +/- 1.4 mm. Under axial compression, the osteotomy caused a significant anterior tibial translation up to 1.9 +/- 2.5 mm (90 degrees ). Under A-P and combined loads, no differences were detected in A-P translation or in situ forces in the cruciates (intact versus osteotomy). Results suggest that small increases in tibial slope do not affect A-P translations or in situ forces in the cruciate ligaments. However, increasing slope causes an anterior shift in tibial resting position that is accentuated under axial loads. This suggests that increasing tibial slope may be beneficial in reducing tibial sag in a PCL-deficient knee, whereas decreasing slope may be protective in an ACL-deficient knee.
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              Neuromusculoskeletal modeling: estimation of muscle forces and joint moments and movements from measurements of neural command.

              This paper provides an overview of forward dynamic neuromusculoskeletal modeling. The aim of such models is to estimate or predict muscle forces, joint moments, and/or joint kinematics from neural signals. This is a four-step process. In the first step, muscle activation dynamics govern the transformation from the neural signal to a measure of muscle activation-a time varying parameter between 0 and 1. In the second step, muscle contraction dynamics characterize how muscle activations are transformed into muscle forces. The third step requires a model of the musculoskeletal geometry to transform muscle forces to joint moments. Finally, the equations of motion allow joint moments to be transformed into joint movements. Each step involves complex nonlinear relationships. The focus of this paper is on the details involved in the first two steps, since these are the most challenging to the biomechanician. The global process is then explained through applications to the study of predicting isometric elbow moments and dynamic knee kinetics.
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                Author and article information

                Contributors
                buchanan@udel.edu
                Journal
                Ann Biomed Eng
                Annals of Biomedical Engineering
                Springer US (Boston )
                0090-6964
                1521-6047
                4 August 2010
                4 August 2010
                January 2011
                : 39
                : 1
                : 110-121
                Affiliations
                Department of Mechanical Engineering, Center for Biomedical Engineering Research, University of Delaware, 126 Spencer Laboratory, Newark, DE 19716 USA
                Author notes

                Associate Editor Peter E. McHugh oversaw the review of this article.

                Article
                131
                10.1007/s10439-010-0131-2
                3010217
                20683675
                7ff16fb0-f8b6-415d-bae2-070918ddd175
                © The Author(s) 2010
                History
                : 15 January 2010
                : 12 July 2010
                Categories
                Article
                Custom metadata
                © Biomedical Engineering Society 2011

                Biomedical engineering
                posterior tibial slope,biomechanical model,emg,mcl
                Biomedical engineering
                posterior tibial slope, biomechanical model, emg, mcl

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