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

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.

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Prediction of knee loading in native knee and total knee arthroplasty : a forward dynamics computational study

10.32469/10355/75041 ◽

2019 ◽

Author(s):

◽

Swithin Samuel Razu

Keyword(s):

Total Knee Arthroplasty ◽

Knee Arthroplasty ◽

Musculoskeletal Model ◽

Contact Forces ◽

Muscle Forces ◽

Model Predictions ◽

Subject Specific ◽

Joint Contact ◽

Total Knee

"The goal of this dissertation is to develop a musculoskeletal model and corroborate model predictions to experimentally measured in vivo knee contact forces, in order to study the biomechanical consequences of two different total knee arthroplasty designs. The two main contributions of this dissertation are: (1) Corroboration to experimental data: The development of an EMG-driven, full-body, musculoskeletal model with subject-specific leg geometries including deformable contacts, ligaments, 6DOF knee joint, and a shoe-floor model that can concurrently predict muscle forces, ligament forces, and joint contact forces. Model predictions of tibiofemoral joint contact forces were evaluated against the subject-specific in vivo measurements from the instrumented TKR for three distinctly different styles of over ground gait. (2) Virtual surgery in TKA: The musculoskeletal modeling methodology was then used to develop a model for one healthy participant with a native knee and then virtually replacing the native knee with fixed-bearing and mobile-bearing total knee arthroplasty designs performing gait and step-up tasks. This approach minimized the biomechanical impact of variations in sex, geometry, implant size, design and positioning, ligament location and tension, and muscle forces found across patients. The differences in biomechanics were compared for the two designs. 1.2 Significance of this Research The world health organization ranks musculoskeletal disorders as the second largest contributor to disability worldwide. Conservative estimates put the national cost of direct care for musculoskeletal disease at $212.7 billion a year [1]. Many people who suffer from neuromuscular or musculoskeletal diseases may benefit from the insights gained from surgery simulations, since musculoskeletal reconstructions are commonly performed on these individuals. Improved surgical outcomes will benefit these individuals not only in the short-term, but also in the long-term, since their future rehabilitation needs may be reduced. For example, although total knee arthroplasty is a common surgical procedure for the treatment of osteoarthritis with over 700,000 procedures performed each year [2], many patients are unhappy with the ultimate results [3]. Ten to 30% of patients report [4] pain, dissatisfaction with function, and the need for further surgery such as revision after the initial surgery resulting in costs exceeding $11 billion [5]. Potentially, simulation studies that quantify the important biomechanical variables will reduce the need for revision surgeries in patients."--Introduction.

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Optimization of Quadriceps Force Distribution for Minimization of Patellofemoral Contact Pressure During a Squat

ASME 2007 Summer Bioengineering Conference ◽

10.1115/sbc2007-176498 ◽

2007 ◽

Author(s):

Aarthi S. Shankar ◽

Trent M. Guess

Keyword(s):

Contact Pressure ◽

Vastus Lateralis ◽

Rectus Femoris ◽

Strong Relationship ◽

Lateral Force ◽

Quadriceps Muscle ◽

Contact Forces ◽

In Vivo Studies ◽

Muscle Forces

Patellofemoral Pain (PFP) syndrome is a very common knee disorder. A possible cause may be excessive lateral force applied by the quadriceps and the patellar tendon producing an abnormal distribution of force and pressure within the patellofemoral joint [1]. EMG and in-vivo studies have been conducted to understand the function of the quadriceps and its relationship with PFP [2,3]. These studies suggest a strong relationship between muscle forces and PFP which originates from high lateral retropatellar contact forces. A dynamic computational model of the knee was developed which includes the quadriceps muscles Rectus Femoris (RF), Vastus Intermedius (VI), Vastus Lateralis (VL), and Vastus Medialis (VM) represented as force vectors. The model can predict retro-patellar contact pressures and the action of the individual quadriceps muscles based on the predicted pressures. The objective of this study was to develop a control system which could optimize the distribution of quadriceps muscle forces to minimize contact pressure between the patella and the femur of the knee during a squat.

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

Journal of Biomechanical Engineering ◽

10.1115/1.4038507 ◽

2018 ◽

Vol 140 (7) ◽

Cited By ~ 4

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.

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

Journal of Biomechanical Engineering ◽

10.1115/1.4032127 ◽

2016 ◽

Vol 138 (2) ◽

Cited By ~ 10

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.

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

Journal of Biomechanical Engineering ◽

10.1115/1.4023457 ◽

2013 ◽

Vol 135 (2) ◽

Cited By ~ 64

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.

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Effect of Joint Center Location on In-Vivo Joint Contact Forces During Walking

ASME 2010 Summer Bioengineering Conference, Parts A and B ◽

10.1115/sbc2010-19353 ◽

2010 ◽

Cited By ~ 5

Author(s):

Yoon-Hyuk Kim ◽

Won-Man Park ◽

Bui Thi Thanh Phuong

Keyword(s):

Articular Cartilage ◽

Joint Arthroplasty ◽

Contact Forces ◽

Implant Design ◽

Muscle Forces ◽

Joint Contact ◽

Joint Center ◽

Muscular Coordination ◽

Accurate Quantification

Estimation of in-vivo joint contact forces and muscle forces during walking is important in order to protect the knee joint from injury or provide adequate rehabilitation and exercise protocols [1]. Assessment of muscle forces can help to understand the neuro-muscular coordination related on neurological problems [2]. Accurate quantification of joint contact forces can help to characterize the contact phenomena of articular cartilage at the joint related on osteoarthritis and to improve the implant design for the longevity of joint arthroplasty [3].

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Update on Grand Challenge Competition to Predict in Vivo Knee Loads

Journal of Biomechanical Engineering ◽

10.1115/1.4023255 ◽

2013 ◽

Vol 135 (2) ◽

Cited By ~ 52

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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