scholarly journals Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking

2016 ◽  
Vol 138 (8) ◽  
Author(s):  
Gil Serrancolí ◽  
Allison L. Kinney ◽  
Benjamin J. Fregly ◽  
Josep M. Font-Llagunes
Keyword(s):  
Contact Force ◽  
Muscle Fiber ◽  
Model Calibration ◽  
Contact Forces ◽  
Patient Specific ◽  
Muscle Forces ◽  
Model Parameter ◽  
Neuromusculoskeletal Model ◽  
Leg Muscle ◽  
Parameter Values

Though walking impairments are prevalent in society, clinical treatments are often ineffective at restoring lost function. For this reason, researchers have begun to explore the use of patient-specific computational walking models to develop more effective treatments. However, the accuracy with which models can predict internal body forces in muscles and across joints depends on how well relevant model parameter values can be calibrated for the patient. This study investigated how knowledge of internal knee contact forces affects calibration of neuromusculoskeletal model parameter values and subsequent prediction of internal knee contact and leg muscle forces during walking. Model calibration was performed using a novel two-level optimization procedure applied to six normal walking trials from the Fourth Grand Challenge Competition to Predict In Vivo Knee Loads. The outer-level optimization adjusted time-invariant model parameter values to minimize passive muscle forces, reserve actuator moments, and model parameter value changes with (Approach A) and without (Approach B) tracking of experimental knee contact forces. Using the current guess for model parameter values but no knee contact force information, the inner-level optimization predicted time-varying muscle activations that were close to experimental muscle synergy patterns and consistent with the experimental inverse dynamic loads (both approaches). For all the six gait trials, Approach A predicted knee contact forces with high accuracy for both compartments (average correlation coefficient r = 0.99 and root mean square error (RMSE) = 52.6 N medial; average r = 0.95 and RMSE = 56.6 N lateral). In contrast, Approach B overpredicted contact force magnitude for both compartments (average RMSE = 323 N medial and 348 N lateral) and poorly matched contact force shape for the lateral compartment (average r = 0.90 medial and −0.10 lateral). Approach B had statistically higher lateral muscle forces and lateral optimal muscle fiber lengths but lower medial, central, and lateral normalized muscle fiber lengths compared to Approach A. These findings suggest that poorly calibrated model parameter values may be a major factor limiting the ability of neuromusculoskeletal models to predict knee contact and leg muscle forces accurately for walking.

scholarly journals Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking

2014 ◽  
Vol 136 (2) ◽  
Author(s):  
Jonathan P. Walter ◽  
Allison L. Kinney ◽  
Scott A. Banks ◽  
Darryl D. D'Lima ◽  
Thor F. Besier ◽  
...  
Keyword(s):  
Contact Force ◽  
Contact Forces ◽  
Muscle Model ◽  
Patient Specific ◽  
Force Prediction ◽  
Model Parameter ◽  
Subject Specific ◽  
Rms Error ◽  
Parameter Values ◽  
Shape Tracking

The ability to predict patient-specific joint contact and muscle forces accurately could improve the treatment of walking-related disorders. Muscle synergy analysis, which decomposes a large number of muscle electromyographic (EMG) signals into a small number of synergy control signals, could reduce the dimensionality and thus redundancy of the muscle and contact force prediction process. This study investigated whether use of subject-specific synergy controls can improve optimization prediction of knee contact forces during walking. To generate the predictions, we performed mixed dynamic muscle force optimizations (i.e., inverse skeletal dynamics with forward muscle activation and contraction dynamics) using data collected from a subject implanted with a force-measuring knee replacement. Twelve optimization problems (three cases with four subcases each) that minimized the sum of squares of muscle excitations were formulated to investigate how synergy controls affect knee contact force predictions. The three cases were: (1) Calibrate+Match where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously matched, (2) Precalibrate+Predict where experimental knee contact forces were predicted using precalibrated muscle model parameters values from the first case, and (3) Calibrate+Predict where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously predicted, all while matching inverse dynamic loads at the hip, knee, and ankle. The four subcases used either 44 independent controls or five synergy controls with and without EMG shape tracking. For the Calibrate+Match case, all four subcases closely reproduced the measured medial and lateral knee contact forces (R2 ≥ 0.94, root-mean-square (RMS) error < 66 N), indicating sufficient model fidelity for contact force prediction. For the Precalibrate+Predict and Calibrate+Predict cases, synergy controls yielded better contact force predictions (0.61 < R2 < 0.90, 83 N < RMS error < 161 N) than did independent controls (-0.15 < R2 < 0.79, 124 N < RMS error < 343 N) for corresponding subcases. For independent controls, contact force predictions improved when precalibrated model parameter values or EMG shape tracking was used. For synergy controls, contact force predictions were relatively insensitive to how model parameter values were calibrated, while EMG shape tracking made lateral (but not medial) contact force predictions worse. For the subject and optimization cost function analyzed in this study, use of subject-specific synergy controls improved the accuracy of knee contact force predictions, especially for lateral contact force when EMG shape tracking was omitted, and reduced prediction sensitivity to uncertainties in muscle model parameter values.

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.

Modeling of Contact Area, Contact Force, and Contact Stiffness of Mechanical Systems With Friction

Tribology ◽  
2005 ◽  
Author(s):  
Jamil Abdo ◽  
Elhanafi Shamseldin
Keyword(s):  
Contact Force ◽  
Contact Area ◽  
Contact Stiffness ◽  
Surface Profile ◽  
Test Area ◽  
Contact Forces ◽  
Profile Data ◽  
Contact Parameter ◽  
Parameter Values ◽  
True Contact Area

It is well recognized that the contact stiffness, true contact area, and the contact force are among the key features in the study of friction system behavior. This paper presents the development of formulae for the mechanical component of dry-friction at the interface of two microscopic rough surfaces. Elastic deformation under the influence of the contact forces is considered. The elastic contact model formulation between interacting asperities is not assumed to occur only at asperity peaks, thus allowing the possibility of oblique contacts wherein the local contact surfaces are no longer parallel to the mean planes of the mating surfaces. It is shown that the approach enables the separation of the contact area into its normal and tangential projections and the contact force into its normal and tangential components. The mathematical model of contact is utilized to develop formulae for normal and tangential contact stiffness. The analytical method is used to estimate contact stiffness components. Contact parameter values for the sample are derived from the surface profile data taken from a 1.0-mm by 10-mm test area. The profile is measured using a Mahr profilometer. A computer program is written and used to analyze the profile data. The analysis yields the asperity density, average asperity radius, and the standard deviation for each test area.

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 Systems biology informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Michelle Przedborski ◽  
Munisha Smalley ◽  
Saravanan Thiyagarajan ◽  
Aaron Goldman ◽  
Mohammad Kohandel
Keyword(s):  
Neural Networks ◽  
Systems Biology ◽  
Clinical Data ◽  
Treatment Protocol ◽  
Response Prediction ◽  
Clinical Findings ◽  
Patient Specific ◽  
Patient Response ◽  
Triple Combination Therapy ◽  
Parameter Values

AbstractAnti-PD-1 immunotherapy has recently shown tremendous success for the treatment of several aggressive cancers. However, variability and unpredictability in treatment outcome have been observed, and are thought to be driven by patient-specific biology and interactions of the patient’s immune system with the tumor. Here we develop an integrative systems biology and machine learning approach, built around clinical data, to predict patient response to anti-PD-1 immunotherapy and to improve the response rate. Using this approach, we determine biomarkers of patient response and identify potential mechanisms of drug resistance. We develop systems biology informed neural networks (SBINN) to calculate patient-specific kinetic parameter values and to predict clinical outcome. We show how transfer learning can be leveraged with simulated clinical data to significantly improve the response prediction accuracy of the SBINN. Further, we identify novel drug combinations and optimize the treatment protocol for triple combination therapy consisting of IL-6 inhibition, recombinant IL-12, and anti-PD-1 immunotherapy in order to maximize patient response. We also find unexpected differences in protein expression levels between response phenotypes which complement recent clinical findings. Our approach has the potential to aid in the development of targeted experiments for patient drug screening as well as identify novel therapeutic targets.

Influence of patellar thickness on patellofemoral contact forces and quadriceps muscle forces

2006 ◽  
Vol 39 ◽  
pp. S492 ◽  
Author(s):  
L.F. Silveira ◽  
C. Bernardes ◽  
G. Portella ◽  
F. Araujo ◽  
J. Loss
Keyword(s):  
Quadriceps Muscle ◽  
Contact Forces ◽  
Muscle Forces ◽  
Patellar Thickness

scholarly journals Dynamics of Multibody Systems With Spherical Clearance Joints

2005 ◽  
Author(s):  
P. Flores ◽  
J. Ambro´sio ◽  
J. C. P. Claro ◽  
H. M. Lankarani
Keyword(s):  
Contact Force ◽  
Multibody Systems ◽  
Contact Forces ◽  
Force Model ◽  
Clearance Joints ◽  
Continuous Contact ◽  
Clearance Joint ◽  
Contact Force Model ◽  
Dynamics Of Multibody Systems ◽  
The Impact

This work deals with a methodology to assess the influence of the spherical clearance joints in spatial multibody systems. The methodology is based on the Cartesian coordinates, being the dynamics of the joint elements modeled as impacting bodies and controlled by contact forces. The impacts and contacts are described by a continuous contact force model that accounts for geometric and mechanical characteristics of the contacting surfaces. The contact force is evaluated as function of the elastic pseudo-penetration between the impacting bodies, coupled with a nonlinear viscous-elastic factor representing the energy dissipation during the impact process. A spatial four bar mechanism is used as an illustrative example and some numerical results are presented, being the efficiency of the developed methodology discussed in the process of their presentation. The results obtained show that the inclusion of clearance joints in the modelization of spatial multibody systems significantly influences the prediction of components’ position and drastically increases the peaks in acceleration and reaction moments at the joints. Moreover, the system’s response clearly tends to be nonperiodic when a clearance joint is included in the simulation.

scholarly journals The influence of muscle pennation angle and cross-sectional area on contact forces in the ankle joint

2016 ◽  
Vol 52 (1) ◽  
pp. 12-23 ◽  
Author(s):  
Ran S Sopher ◽  
Andrew A Amis ◽  
D Ceri Davies ◽  
Jonathan RT Jeffers
Keyword(s):  
Pennation Angle ◽  
Muscle Architecture ◽  
Cross Sectional Area ◽  
Contact Forces ◽  
Sectional Area ◽  
Muscle Forces ◽  
Cross Sectional ◽  
Contact Loads ◽  
The Mean ◽  
And Function

Data about a muscle’s fibre pennation angle and physiological cross-sectional area are used in musculoskeletal modelling to estimate muscle forces, which are used to calculate joint contact forces. For the leg, muscle architecture data are derived from studies that measured pennation angle at the muscle surface, but not deep within it. Musculoskeletal models developed to estimate joint contact loads have usually been based on the mean values of pennation angle and physiological cross-sectional area. Therefore, the first aim of this study was to investigate differences between superficial and deep pennation angles within each muscle acting over the ankle and predict how differences may influence muscle forces calculated in musculoskeletal modelling. The second aim was to investigate how inter-subject variability in physiological cross-sectional area and pennation angle affects calculated ankle contact forces. Eight cadaveric legs were dissected to excise the muscles acting over the ankle. The mean surface and deep pennation angles, fibre length and physiological cross-sectional area were measured. Cluster analysis was applied to group the muscles according to their architectural characteristics. A previously validated OpenSim model was used to estimate ankle muscle forces and contact loads using architecture data from all eight limbs. The mean surface pennation angle for soleus was significantly greater (54%) than the mean deep pennation angle. Cluster analysis revealed three groups of muscles with similar architecture and function: deep plantarflexors and peroneals, superficial plantarflexors and dorsiflexors. Peak ankle contact force was predicted to occur before toe-off, with magnitude greater than five times bodyweight. Inter-specimen variability in contact force was smallest at peak force. These findings will help improve the development of experimental and computational musculoskeletal models by providing data to estimate force based on both surface and deep pennation angles. Inter-subject variability in muscle architecture affected ankle muscle and contact loads only slightly. The link between muscle architecture and function contributes to the understanding of the relationship between muscle structure and function.

Functional Evaluation of a Personalized Orthosis for Knee Osteoarthritis - A Motion Capture Analysis

2021 ◽  
Author(s):  
Martin Huber ◽  
Matthew Eschbach ◽  
Kazem Kazerounian ◽  
Horea T. Ilies
Keyword(s):  
Knee Joint ◽  
Knee Osteoarthritis ◽  
Motion Capture ◽  
Inverse Dynamics ◽  
Statistical Significance ◽  
Knee Kinematics ◽  
Contact Forces ◽  
Patient Specific ◽  
Functional Evaluation ◽  
Shock Absorption

Abstract Knee osteoarthritis (OA) is a disease that compromises the cartilage inside the knee joint, resulting in pain and impaired mobility. Bracing is a common treatment, however currently prescribed braces cannot treat bicompartmental knee OA, fail to consider the muscle weakness that typically accompanies the disease, and utilize hinges that restrict the knee's natural biomechanics. We have developed and evaluated a brace which addresses these shortcomings. This process has respected three principal design goals: reducing the load experienced across the entire knee joint, generating a supportive moment to aid the muscles in shock absorption, and interfering minimally with gait kinematics. Load reduction is achieved via the compression of medial and lateral leaf springs, and magnetorheological dampers provide the supportive moment during knee loading. A novel, personalized joint mechanism replaces a traditional hinge to reduce interference with knee kinematics. Using motion capture gait analysis, we evaluated the basic functionality of a prototype device. We calculated, via inverse dynamics analysis, the reaction forces at the knee joint and the moments generated by the leg muscles during gait. Comparing these values between braced and unbraced trials allowed us to evaluate the system's effectiveness. Kinematic measurements showed the extent to which the brace interfered with natural gait characteristics. Of the three design goals: a reduction in knee contact forces was demonstrated; increased shock absorption was observed, but not to statistical significance; and natural gait was largely preserved. The techniques presented in this paper could lead to improved OA treatment through patient-specific braces.

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