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.

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.

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.

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.

scholarly journals Electromyography-Driven Forward Dynamics Simulation to Estimate In Vivo Joint Contact Forces During Normal, Smooth, and Bouncy Gaits

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Swithin S. Razu ◽  
Trent M. Guess
Keyword(s):  
Contact Force ◽  
Temporal Patterns ◽  
Musculoskeletal Model ◽  
Contact Forces ◽  
Dynamics Simulation ◽  
Muscle Forces ◽  
Model Predictions ◽  
Subject Specific ◽  
Full Body

Computational models that predict in vivo joint loading and muscle forces can potentially enhance and augment our knowledge of both typical and pathological gaits. To adopt such models into clinical applications, studies validating modeling predictions are essential. This study created a full-body musculoskeletal model using data from the “Sixth Grand Challenge Competition to Predict in vivo Knee Loads.” This model incorporates subject-specific geometries of the right leg in order to concurrently predict knee contact forces, ligament forces, muscle forces, and ground contact forces. The objectives of this paper are twofold: (1) to describe an electromyography (EMG)-driven modeling methodology to predict knee contact forces and (2) to validate model predictions by evaluating the model predictions against known values for a patient with an instrumented total knee replacement (TKR) for three distinctly different gait styles (normal, smooth, and bouncy gaits). The model integrates a subject-specific knee model onto a previously validated generic full-body musculoskeletal model. The combined model included six degrees-of-freedom (6DOF) patellofemoral and tibiofemoral joints, ligament forces, and deformable contact forces with viscous damping. The foot/shoe/floor interactions were modeled by incorporating shoe geometries to the feet. Contact between shoe segments and the floor surface was used to constrain the shoe segments. A novel EMG-driven feedforward with feedback trim motor control strategy was used to concurrently estimate muscle forces and knee contact forces from standard motion capture data collected on the individual subject. The predicted medial, lateral, and total tibiofemoral forces represented the overall measured magnitude and temporal patterns with good root-mean-squared errors (RMSEs) and Pearson's correlation (p2). The model accuracy was high: medial, lateral, and total tibiofemoral contact force RMSEs = 0.15, 0.14, 0.21 body weight (BW), and (0.92 < p2 < 0.96) for normal gait; RMSEs = 0.18 BW, 0.21 BW, 0.29 BW, and (0.81 < p2 < 0.93) for smooth gait; and RMSEs = 0.21 BW, 0.22 BW, 0.33 BW, and (0.86 < p2 < 0.95) for bouncy gait, respectively. Overall, the model captured the general shape, magnitude, and temporal patterns of the contact force profiles accurately. Potential applications of this proposed model include predictive biomechanics simulations, design of TKR components, soft tissue balancing, and surgical simulation.

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.

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.

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.

Evaluation of Regression Equations for Medial and Lateral Contact Force From Instrumented Knee Implant Data

2011 ◽  
Author(s):  
Andrew J. Meyer ◽  
Darryl D. D’Lima ◽  
Scott A. Banks ◽  
James Coburn ◽  
Melinda Harman ◽  
...  
Keyword(s):  
Contact Force ◽  
Computational Models ◽  
Contact Forces ◽  
Regression Equations ◽  
Quick Method ◽  
Lateral Contact ◽  
Knee Replacements ◽  
Clinically Significant ◽  
Neuromuscular Coordination

The ability to predict medial and lateral contact forces accurately in the knee could be useful for research on neuromuscular coordination, the development of treatments for knee osteoarthritis, and the design of knee replacements with improved durability and functionality. To improve their ability to predict medial and lateral contact forces, researchers have recently developed knee implants capable of measuring four [1] or six [2] in vivo loads applied to the tibia. These implants provide data that is useful for validating in vivo knee load predictions from computational models. A difficulty in using these experimental measurements is translating them into clinically significant measurements, i.e. medial and lateral contact force. A simple and quick method for finding these contact forces from the experimental implant data would be ideal.

Grand Challenge Competition: A Parametric Numerical Model to Predict In Vivo Medial and Lateral Knee Forces in Walking Gaits

2012 ◽  
Author(s):  
Christopher B. Knowlton ◽  
Markus A. Wimmer ◽  
Hannah J. Lundberg
Keyword(s):  
Knee Joint ◽  
Computational Models ◽  
Soft Tissues ◽  
Numerical Models ◽  
Contact Forces ◽  
Grand Challenge ◽  
Total Knee Replacements ◽  
Lateral Contact ◽  
Total Knee

Numerical models are necessary to estimate forces through the knee joint during activities of daily living. However, the numerous muscles and soft tissues crossing the knee joint result in a computationally indeterminate problem. The recent availability of measured contact force data from telemeterized total knee replacements (TKRs) has given researchers the chance to validate models, but telemeterized TKRs represent only a few patients with a specific implant type. Computational models remain necessary to bridge the gap between the small instrumented patient population with a particular implant and larger patient populations executing various activities. Abstracted gait data from another lab tests the versatility of any model to accurately predict forces of TKR patients performing a variety of gaits with disparate implant types. In this study, we calculate and examine the differences between medial and lateral contact forces in level walking and medial thrust gait trials from a freely provided dataset1.

Validation of Knee Load Predictions During a Dual Limb Squat and Calfrise

2012 ◽  
Author(s):  
Trent M. Guess ◽  
Antonis Stylianou ◽  
Mohammad Kia
Keyword(s):  
Reaction Force ◽  
Contact Forces ◽  
Grand Challenge ◽  
Ground Reaction Forces ◽  
Data Set ◽  
Prosthetic Knee ◽  
Knee Loading ◽  
Model Predictions ◽  
Reaction Forces

Knowledge of knee loading would benefit prosthetic design, development of tissue engineered materials, orthopedic repair, and management of degenerative joint diseases such as osteoarthritis. Musculoskeletal modeling provides a method for estimating in vivo joint loading, but validation of model predictions is challenging. Data provided by the “Grand Challenge Competition to Predict In-Vivo Knee Loads” for the 2012 American Society of Mechanical Engineers Summer Bioengineering Conference [1] provides data from an instrumented prosthetic knee that can be used to validate load predictions. The Grand Challenge data set includes implant and bone geometries, motion, ground reaction forces, electromyography (EMG) as well as measured knee loading. Presented here are muscle driven forward dynamics simulations with a prosthetic knee for two of the calibration gait trials (SC_2legsquat and SC_calfrise) provided with the Grand Challenge data set. The calibration trials include the instrumented knee measurements and are provided to help “calibrate” models used in the Grand Challenge competition. Inputs to model simulations were experimental marker motion and outputs included muscle force, ground reaction forces, ligament forces, contact forces, and knee loading. Experimental measurements of knee loading, ground reaction force, and muscle activations were compared to model predictions.

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