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 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.

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.

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.

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.

Toe-Out Gait Decreases the Second Peak of the Medial Knee Contact Force

2015 ◽  
Vol 31 (4) ◽  
pp. 275-280 ◽  
Author(s):  
Shinya Ogaya ◽  
Hisashi Naito ◽  
Akira Iwata ◽  
Yumi Higuchi ◽  
Satoshi Fuchioka ◽  
...  
Keyword(s):  
Contact Force ◽  
Simulation Analysis ◽  
Cartilage Damage ◽  
Reaction Force ◽  
Muscle Tension ◽  
Contact Forces ◽  
Knee Adduction Moment ◽  
Joint Reaction Force ◽  
Medial Knee ◽  
Second Peak

Toe-out angle alternation is a potential tactic for decreasing the knee adduction moment during walking. Published reports have not examined the medial knee contact force during the toe-out gait, although it is a factor affecting knee articular cartilage damage. This study investigated the effects of increased toe-out angle on the medial knee contact force, using musculoskeletal simulation analysis. For normal and toe-out gaits in 18 healthy subjects, the muscle tension forces were simulated based on the joint moments and ground reaction forces with optimization process. The medial knee contact force during stance phase was determined using the sum of the muscle force and joint reaction force components. The first and second peaks of the medial knee contact force were compared between the gaits. The toe-out gait showed a significant decrease in the medial knee contact force at the second peak, compared with the normal gait. In contrast, the medial knee contact forces at the first peak were not significantly different between the gaits. These results suggest that the toe-out gait is beneficial for decreasing the second peak of the medial knee contact force.

The Stationary Configuration of the Knee

2013 ◽  
Vol 103 (2) ◽  
pp. 126-135 ◽  
Author(s):  
Wangdo Kim ◽  
Antonio P. Veloso ◽  
Veronica E. Vleck ◽  
Carlos Andrade ◽  
Sean S. Kohles
Keyword(s):  
Knee Joint ◽  
Inverse Dynamics ◽  
Reaction Force ◽  
Reciprocal System ◽  
Grand Challenge ◽  
Joint Movement ◽  
Structural Components ◽  
Data Set ◽  
And Cartilage

Background: Ligaments and cartilage contact contribute to the mechanical constraints in the knee joints. However, the precise influence of these structural components on joint movement, especially when the joint constraints are computed using inverse dynamics solutions, is not clear. Methods: We present a mechanical characterization of the connections between the infinitesimal twist of the tibia and the femur due to restraining forces in the specific tissue components that are engaged and responsible for such motion. These components include the anterior cruciate, posterior cruciate, medial collateral, and lateral collateral ligaments and cartilage contact surfaces in the medial and lateral compartments. Their influence on the bony rotation about the instantaneous screw axis is governed by restraining forces along the constraints explored using the principle of reciprocity. Results: Published kinetic and kinematic joint data (American Society of Mechanical Engineers Grand Challenge Competition to Predict In Vivo Knee Loads) are applied to define knee joint function for verification using an available instrumented knee data set. We found that the line of the ground reaction force (GRF) vector is very close to the axis of the knee joint. It aligns the knee joint with the GRF such that the reaction torques are eliminated. The reaction to the GRF will then be carried by the structural components of the knee instead. Conclusions: The use of this reciprocal system introduces a new dimension of foot loading to the knee axis alignment. This insight shows that locating knee functional axes is equivalent to the static alignment measurement. This method can be used for the optimal design of braces and orthoses for conservative treatment of knee osteoarthritis. (J Am Podiatr Med Assoc 103(2): 126–135, 2013)

Correlation Between In Vivo Knee Contact Forces and External Measures During Gait

2012 ◽  
Author(s):  
Andrew J. Meyer ◽  
Darryl D. D’Lima ◽  
Scott A. Banks ◽  
David G. Lloyd ◽  
Thor F. Besier ◽  
...  
Keyword(s):  
Knee Osteoarthritis ◽  
Contact Force ◽  
Inverse Dynamics ◽  
Contact Forces ◽  
Clinical Environment ◽  
Total Knee ◽  
Surrogate Measures ◽  
Force Data ◽  
External Measure

Researchers have sought to explain the development and progression of knee osteoarthritis using in vivo estimates of knee contact forces. Unfortunately, it is not possible to measure knee contact forces in a clinical environment. Thus, studies often estimate knee contact forces using a variety of external measures. For example, inverse dynamics knee loads such as the adduction moment and superior force are frequently used as surrogate measures of medial and total knee contact force, respectively. Contraction of muscles spanning the knee is believed to increase knee contact force, and hence muscle electromyographic (EMG) signals are another external measure that may be indicative of internal contact force. The recent development of instrumented knee implants, such as the eTibia design [1], has provided access to in vivo knee contact force data during gait and other activities. However, few studies have correlated inverse dynamics loads, and none have correlated EMG signals, with total, medial, and lateral in vivo knee contact forces.

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.

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.

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