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

2016 ◽  
Vol 138 (2) ◽  
Author(s):  
Ziyun Ding ◽  
Daniel Nolte ◽  
Chui Kit Tsang ◽  
Daniel J. Cleather ◽  
Angela E. Kedgley ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Contact Forces ◽  
Coefficient Of Determination ◽  
Patient Specific ◽  
Grand Challenge ◽  
Sit To Stand ◽  
Normal Gait ◽  
Musculoskeletal Models ◽  
Mean Square Errors

Segment-based musculoskeletal models allow the prediction of muscle, ligament, and joint forces without making assumptions regarding joint degrees-of-freedom (DOF). The dataset published for the “Grand Challenge Competition to Predict in vivo Knee Loads” 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 “smooth” and “bouncy” gait trials were predicted using a customized patient-specific 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 segment-based approach to motion tracking artifact. For validation, the improved model was applied to normal gait, squat, and sit-to-stand for three subjects. Comparisons of the predictions with measured contact forces showed that segment-based musculoskeletal models using patient-specific 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 sit-to-stand tasks. This is comparable to the best validations in the literature using alternative models.

Intra-Articular Knee Contact Force Estimation During Walking Using Force-Reaction Elements and Subject-Specific Joint Model2

2016 ◽  
Vol 138 (2) ◽  
Author(s):  
Yihwan Jung ◽  
Cong-Bo Phan ◽  
Seungbum Koo
Keyword(s):  
Contact Force ◽  
Inverse Dynamics ◽  
Reaction Force ◽  
Muscle Tension ◽  
Contact Forces ◽  
Contact Model ◽  
Grand Challenge ◽  
Subject Specific ◽  
Mean Square Errors

Joint contact forces measured with instrumented knee implants have not only revealed general patterns of joint loading but also showed individual variations that could be due to differences in anatomy and joint kinematics. Musculoskeletal human models for dynamic simulation have been utilized to understand body kinetics including joint moments, muscle tension, and knee contact forces. The objectives of this study were to develop a knee contact model which can predict knee contact forces using an inverse dynamics-based optimization solver and to investigate the effect of joint constraints on knee contact force prediction. A knee contact model was developed to include 32 reaction force elements on the surface of a tibial insert of a total knee replacement (TKR), which was embedded in a full-body musculoskeletal model. Various external measurements including motion data and external force data during walking trials of a subject with an instrumented knee implant were provided from the Sixth Grand Challenge Competition to Predict in vivo Knee Loads. Knee contact forces in the medial and lateral portions of the instrumented knee implant were also provided for the same walking trials. A knee contact model with a hinge joint and normal alignment could predict knee contact forces with root mean square errors (RMSEs) of 165 N and 288 N for the medial and lateral portions of the knee, respectively, and coefficients of determination (R2) of 0.70 and −0.63. When the degrees-of-freedom (DOF) of the knee and locations of leg markers were adjusted to account for the valgus lower-limb alignment of the subject, RMSE values improved to 144 N and 179 N, and R2 values improved to 0.77 and 0.37, respectively. The proposed knee contact model with subject-specific joint model could predict in vivo knee contact forces with reasonable accuracy. This model may contribute to the development and improvement of knee arthroplasty.

Influence of the Level of Muscular Redundancy on the Validity of a Musculoskeletal Model

2016 ◽  
Vol 138 (2) ◽  
Author(s):  
Florent Moissenet ◽  
Laurence Chèze ◽  
Raphaël Dumas
Keyword(s):  
Gait Pattern ◽  
Musculoskeletal Model ◽  
Contact Forces ◽  
Generic Model ◽  
Grand Challenge ◽  
Contact Points ◽  
Lateral Contact ◽  
Musculoskeletal Models ◽  
Joint Contact

While recent literature has clearly demonstrated that an extensive personalization of the musculoskeletal models was necessary to reach high accuracy, several components of the generic models may be further investigated before defining subject-specific parameters. Among others, the choice in muscular geometry and thus the level of muscular redundancy in the model may have a noticeable influence on the predicted musculotendon and joint contact forces. In this context, the aim of this study was to investigate if the level of muscular redundancy can contribute or not to reduce inaccuracies in tibiofemoral contact forces predictions. For that, the dataset disseminated through the Sixth Grand Challenge Competition to Predict In Vivo Knee Loads was applied to a versatile 3D lower limb musculoskeletal model in which two muscular geometries (i.e., two different levels of muscular redundancy) were implemented. This dataset provides tibiofemoral implant measurements for both medial and lateral compartments and thus allows evaluation of the validity of the model predictions. The results suggest that an increase of the level of muscular redundancy corresponds to a better accuracy of total tibiofemoral contact force whatever the gait pattern investigated. However, the medial and lateral contact forces ratio and accuracy were not necessarily improved when increasing the level of muscular redundancy and may thus be attributed to other parameters such as the location of contact points. To conclude, the muscular geometry, among other components of the generic model, has a noticeable impact on joint contact forces predictions and may thus be correctly chosen even before trying to personalize the model.

Tibio-Femoral Contact Force During Gait: An Iterative Method Using EMG-Constrained Multi-Body Simulation and Finite Element Analysis

2013 ◽  
Author(s):  
Silvia Pianigiani ◽  
Friedl De Groote ◽  
Lennart Scheys ◽  
Pierre Gillen ◽  
Luc Labey ◽  
...  
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Degrees Of Freedom ◽  
Image Data ◽  
Contact Forces ◽  
Patient Specific ◽  
Optimization Approach ◽  
Element Analysis ◽  
Force Calculation

In this study, we present an innovative methodology (Figure 1) to calculate patient specific tibio-femoral (TF) contact forces by integrating medical image data, 3D skin-mounted marker trajectories, ground reaction forces, electromyography (EMG) data and finite element analysis (FEA). The muscle redundancy problem is solved through an EMG-constrained optimization approach. Calculated muscle forces are input to a FEA to calculate TF contact forces. Kinematics of the degrees of freedom (DOFs) of the knee that cannot be accurately assessed from the trajectories of skin-mounted markers, are estimated using a novel iterative procedure which combines muscle force calculation with dynamic FEA. The presented methodology is applied to analyze TF contact forces of a walking trial performed on an instrumented treadmill of which the speed was sequentially ramped up and down. The results presented in this abstract will be validated against the in-vivo measured TF contact forces.

scholarly journals Update on Grand Challenge Competition to Predict in Vivo Knee Loads

2013 ◽  
Vol 135 (2) ◽  
Author(s):  
Allison L. Kinney ◽  
Thor F. Besier ◽  
Darryl D. D'Lima ◽  
Benjamin J. Fregly
Keyword(s):  
Contact Force ◽  
Contact Forces ◽  
Single Subject ◽  
Grand Challenge ◽  
Muscle Forces ◽  
Data Set ◽  
Lateral Contact ◽  
Musculoskeletal Models ◽  
Rms Errors

Validation is critical if clinicians are to use musculoskeletal models to optimize treatment of individual patients with a variety of musculoskeletal disorders. This paper provides an update on the annual Grand Challenge Competition to Predict in Vivo Knee Loads, a unique opportunity for direct validation of knee contact forces and indirect validation of knee muscle forces predicted by musculoskeletal models. Three competitions (2010, 2011, and 2012) have been held at the annual American Society of Mechanical Engineers Summer Bioengineering Conference, and two more competitions are planned for the 2013 and 2014 conferences. Each year of the competition, a comprehensive data set collected from a single subject implanted with a force-measuring knee replacement is released. Competitors predict medial and lateral knee contact forces for two gait trials without knowledge of the experimental knee contact force measurements. Predictions are evaluated by calculating root-mean-square (RMS) errors and R2 values relative to the experimentally measured medial and lateral contact forces. For the first three years of the competition, competitors used a variety of methods to predict knee contact and muscle forces, including static and dynamic optimization, EMG-driven models, and parametric numerical models. Overall, errors in predicted contact forces were comparable across years, with average RMS errors for the four competition winners ranging from 229 N to 312 N for medial contact force and from 238 N to 326 N for lateral contact force. Competitors generally predicted variations in medial contact force (highest R2 = 0.91) better than variations in lateral contact force (highest R2 = 0.70). Thus, significant room for improvement exists in the remaining two competitions. The entire musculoskeletal modeling community is encouraged to use the competition data and models for their own model validation efforts.

scholarly journals Concurrent Prediction of Muscle and Tibiofemoral Contact Forces During Treadmill Gait

2014 ◽  
Vol 136 (2) ◽  
Author(s):  
Trent M. Guess ◽  
Antonis P. Stylianou ◽  
Mohammad Kia
Keyword(s):  
Femoral Component ◽  
Gait Cycle ◽  
Musculoskeletal Model ◽  
Contact Forces ◽  
Grand Challenge ◽  
Subject Specific ◽  
The Right ◽  
Anterior Posterior ◽  
Rms Errors

Detailed knowledge of knee kinematics and dynamic loading is essential for improving the design and outcomes of surgical procedures, tissue engineering applications, prosthetics design, and rehabilitation. This study used publicly available data provided by the “Grand Challenge Competition to Predict in-vivo Knee Loads” for the 2013 American Society of Mechanical Engineers Summer Bioengineering Conference (Fregly et al., 2012, “Grand Challenge Competition to Predict in vivo Knee Loads,” J. Orthop. Res., 30, pp. 503–513) to develop a full body, musculoskeletal model with subject specific right leg geometries that can concurrently predict muscle forces, ligament forces, and knee and ground contact forces. The model includes representation of foot/floor interactions and predicted tibiofemoral joint loads were compared to measured tibial loads for two different cycles of treadmill gait. The model used anthropometric data (height and weight) to scale the joint center locations and mass properties of a generic model and then used subject bone geometries to more accurately position the hip and ankle. The musculoskeletal model included 44 muscles on the right leg, and subject specific geometries were used to create a 12 degrees-of-freedom anatomical right knee that included both patellofemoral and tibiofemoral articulations. Tibiofemoral motion was constrained by deformable contacts defined between the tibial insert and femoral component geometries and by ligaments. Patellofemoral motion was constrained by contact between the patellar button and femoral component geometries and the patellar tendon. Shoe geometries were added to the feet, and shoe motion was constrained by contact between three shoe segments per foot and the treadmill surface. Six-axis springs constrained motion between the feet and shoe segments. Experimental motion capture data provided input to an inverse kinematics stage, and the final forward dynamics simulations tracked joint angle errors for the left leg and upper body and tracked muscle length errors for the right leg. The one cycle RMS errors between the predicted and measured tibia contact were 178 N and 168 N for the medial and lateral sides for the first gait cycle and 209 N and 228 N for the medial and lateral sides for the faster second gait cycle. One cycle RMS errors between predicted and measured ground reaction forces were 12 N, 13 N, and 65 N in the anterior-posterior, medial-lateral, and vertical directions for the first gait cycle and 43 N, 15 N, and 96 N in the anterior-posterior, medial-lateral, and vertical directions for the second gait cycle.

Evaluation of Predicted Knee Joint Muscle Forces During Gait Using an Instrumented Knee Implant

2008 ◽  
Author(s):  
Justin W. Fernandez ◽  
Hyung J. Kim ◽  
Massoud Akbarshahi ◽  
Jonathan P. Walter ◽  
Benjamin J. Fregly ◽  
...  
Keyword(s):  
Contact Forces ◽  
In Vivo Studies ◽  
Muscle Forces ◽  
Model Calculations ◽  
Musculoskeletal Models ◽  
Model Predictions ◽  
Joint Contact ◽  
Knee Muscle

Many studies have used musculoskeletal models to predict in vivo muscle forces at the knee during gait [1, 2]. Unfortunately, quantitative assessment of the model calculations is often impracticable. Various indirect methods have been used to evaluate the accuracy of model predictions, including comparisons against measurements of muscle activity, joint kinematics, ground reaction forces, and joint moments. In a recent study, an instrumented hip implant was used to validate calculations of hip contact forces directly [3]. The same model was subsequently used to validate model calculations of tibiofemoral loading during gait [4]. Instrumented knee implants have also been used in in vitro and in vivo studies to quantify differences in biomechanical performance between various TKR designs [5, 6]. The main aim of the present study was to evaluate model predictions of knee muscle forces by direct comparison with measurements obtained from an instrumented knee implant. Calculations of muscle and joint-contact loading were performed for level walking at slow, normal, and fast speeds.

Muscle Synergy Constraints Improve Prediction of Knee Contact Force During Gait

2013 ◽  
Author(s):  
Benjamin J. Fregly ◽  
Jonathan P. Walter ◽  
Allison L. Kinney ◽  
Scott A. Banks ◽  
Darryl D. D’Lima ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Neural Control ◽  
Optimization Methods ◽  
Internal Force ◽  
Contact Forces ◽  
Muscle Synergy ◽  
Patient Specific ◽  
Joint Contact ◽  
Specific Muscle ◽  
Clinical Conditions

Knowledge of patient-specific muscle and joint contact forces during activities of daily living could improve the treatment of movement-related disorders (e.g., osteoarthritis, stroke, cerebral palsy, Parkinson’s disease). Unfortunately, it is currently impossible to measure these quantities directly under common clinical conditions, and calculation of these quantities using computer models is limited by the redundant nature of human neural control (i.e., more muscles than theoretically necessary to actuate the available degrees of freedom in the skeleton). Walking is a particularly important task to understand, since loss of mobility is associated with increased morbidity and decreased quality of life [1]. Though numerous musculoskeletal computer modeling studies have used optimization methods to resolve the neural control redundancy problem, these estimates remain unvalidated due to the lack of internal force measurements that can be used for validation purposes.

scholarly journals An Electromyogram-Driven Musculoskeletal Model of the Knee to Predict in Vivo Joint Contact Forces During Normal and Novel Gait Patterns

2013 ◽  
Vol 135 (2) ◽  
Author(s):  
Kurt Manal ◽  
Thomas S. Buchanan
Keyword(s):  
Knee Joint ◽  
Contact Force ◽  
Contact Forces ◽  
Grand Challenge ◽  
Normal Walking ◽  
Gait Patterns ◽  
Internal Joint ◽  
Model Predictions ◽  
Joint Contact

Computational models that predict internal joint forces have the potential to enhance our understanding of normal and pathological movement. Validation studies of modeling results are necessary if such models are to be adopted by clinicians to complement patient treatment and rehabilitation. The purposes of this paper are: (1) to describe an electromyogram (EMG)-driven modeling approach to predict knee joint contact forces, and (2) to evaluate the accuracy of model predictions for two distinctly different gait patterns (normal walking and medial thrust gait) against known values for a patient with a force recording knee prosthesis. Blinded model predictions and revised model estimates for knee joint contact forces are reported for our entry in the 2012 Grand Challenge to predict in vivo knee loads. The EMG-driven model correctly predicted that medial compartment contact force for the medial thrust gait increased despite the decrease in knee adduction moment. Model accuracy was high: the difference in peak loading was less than 0.01 bodyweight (BW) with an R2 = 0.92. The model also predicted lateral loading for the normal walking trial with good accuracy exhibiting a peak loading difference of 0.04 BW and an R2 = 0.44. Overall, the EMG-driven model captured the general shape and timing of the contact force profiles and with accurate input data the model estimated joint contact forces with sufficient accuracy to enhance the interpretation of joint loading beyond what is possible from data obtained from standard motion capture studies.

A Subject-Specific Musculoskeletal Modeling Framework to Predict In Vivo Mechanics of Total Knee Arthroplasty

2015 ◽  
Vol 137 (2) ◽  
Author(s):  
Marco A. Marra ◽  
Valentine Vanheule ◽  
René Fluit ◽  
Bart H. F. J. M. Koopman ◽  
John Rasmussen ◽  
...  
Keyword(s):  
Knee Kinematics ◽  
Contact Forces ◽  
Grand Challenge ◽  
Modeling Framework ◽  
Trial Simulation ◽  
Subject Specific ◽  
Joint Contact ◽  
Total Knee ◽  
The Right

Musculoskeletal (MS) models should be able to integrate patient-specific MS architecture and undergo thorough validation prior to their introduction into clinical practice. We present a methodology to develop subject-specific models able to simultaneously predict muscle, ligament, and knee joint contact forces along with secondary knee kinematics. The MS architecture of a generic cadaver-based model was scaled using an advanced morphing technique to the subject-specific morphology of a patient implanted with an instrumented total knee arthroplasty (TKA) available in the fifth “grand challenge competition to predict in vivo knee loads” dataset. We implemented two separate knee models, one employing traditional hinge constraints, which was solved using an inverse dynamics technique, and another one using an 11-degree-of-freedom (DOF) representation of the tibiofemoral (TF) and patellofemoral (PF) joints, which was solved using a combined inverse dynamic and quasi-static analysis, called force-dependent kinematics (FDK). TF joint forces for one gait and one right-turn trial and secondary knee kinematics for one unloaded leg-swing trial were predicted and evaluated using experimental data available in the grand challenge dataset. Total compressive TF contact forces were predicted by both hinge and FDK knee models with a root-mean-square error (RMSE) and a coefficient of determination (R2) smaller than 0.3 body weight (BW) and equal to 0.9 in the gait trial simulation and smaller than 0.4 BW and larger than 0.8 in the right-turn trial simulation, respectively. Total, medial, and lateral TF joint contact force predictions were highly similar, regardless of the type of knee model used. Medial (respectively lateral) TF forces were over- (respectively, under-) predicted with a magnitude error of M < 0.2 (respectively > −0.4) in the gait trial, and under- (respectively, over-) predicted with a magnitude error of M > −0.4 (respectively < 0.3) in the right-turn trial. Secondary knee kinematics from the unloaded leg-swing trial were overall better approximated using the FDK model (average Sprague and Geers' combined error C = 0.06) than when using a hinged knee model (C = 0.34). The proposed modeling approach allows detailed subject-specific scaling and personalization and does not contain any nonphysiological parameters. This modeling framework has potential applications in aiding the clinical decision-making in orthopedics procedures and as a tool for virtual implant design.

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