R-3 Repository :: Browsing by Author "Fregly, Benjamin J."

Browsing by Author "Fregly, Benjamin J."

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A computational framework for simultaneous estimation of muscle and joint contact forces and body motion using optimization and surrogate modeling
(Elsevier, 2018) Eskinazi, Ilan; Fregly, Benjamin J.
Concurrent estimation of muscle activations, joint contact forces, and joint kinematics by means of gradient-based optimization of musculoskeletal models is hindered by computationally expensive and non-smooth joint contact and muscle wrapping algorithms. We present a framework that simultaneously speeds up computation and removes sources of non-smoothness from muscle force optimizations using a combination of parallelization and surrogate modeling, with special emphasis on a novel method for modeling joint contact as a surrogate model of a static analysis. The approach allows one to efficiently introduce elastic joint contact models within static and dynamic optimizations of human motion. We demonstrate the approach by performing two optimizations, one static and one dynamic, using a pelvis-leg musculoskeletal model undergoing a gait cycle. We observed convergence on the order of seconds for a static optimization time frame and on the order of minutes for an entire dynamic optimization. The presented framework may facilitate model-based efforts to predict how planned surgical or rehabilitation interventions will affect post-treatment joint and muscle function.
A computational method for estimating trunk muscle activations during gait using lower extremity muscle synergies
(Frontiers Media S.A., 2022) Li, Geng; Ao, Di; Vega, Marleny M.; Shourijeh, Mohammad S.; Zandiyeh, Payam; Chang, Shuo-Hsiu; Lewis, Valerae O.; Dunbar, Nicholas J.; Babazadeh-Naseri, Ata; Baines, Andrew J.; Fregly, Benjamin J.; Rice Computational Neuromechanics Laboratory
One of the surgical treatments for pelvic sarcoma is the restoration of hip function with a custom pelvic prosthesis after cancerous tumor removal. The orthopedic oncologist and orthopedic implant company must make numerous often subjective decisions regarding the design of the pelvic surgery and custom pelvic prosthesis. Using personalized musculoskeletal computer models to predict post-surgery walking function and custom pelvic prosthesis loading is an emerging method for making surgical and custom prosthesis design decisions in a more objective manner. Such predictions would necessitate the estimation of forces generated by muscles spanning the lower trunk and all joints of the lower extremities. However, estimating trunk and leg muscle forces simultaneously during walking based on electromyography (EMG) data remains challenging due to the limited number of EMG channels typically used for measurement of leg muscle activity. This study developed a computational method for estimating unmeasured trunk muscle activations during walking using lower extremity muscle synergies. To facilitate the calibration of an EMG-driven model and the estimation of leg muscle activations, EMG data were collected from each leg. Using non-negative matrix factorization, muscle synergies were extracted from activations of leg muscles. On the basis of previous studies, it was hypothesized that the time-varying synergy activations were shared between the trunk and leg muscles. The synergy weights required to reconstruct the trunk muscle activations were determined through optimization. The accuracy of the synergy-based method was dependent on the number of synergies and optimization formulation. With seven synergies and an increased level of activation minimization, the estimated activations of the erector spinae were strongly correlated with their measured activity. This study created a custom full-body model by combining two existing musculoskeletal models. The model was further modified and heavily personalized to represent various aspects of the pelvic sarcoma patient, all of which contributed to the estimation of trunk muscle activations. This proposed method can facilitate the prediction of post-surgery walking function and pelvic prosthesis loading, as well as provide objective evaluations for surgical and prosthesis design decisions.
Changes in walking function and neural control following pelvic cancer surgery with reconstruction
(Frontiers, 2024) Li, Geng; Ao, Di; Vega, Marleny M.; Zandiyeh, Payam; Chang, Shuo-Hsiu; Penny, Alexander N.; Lewis, Valerae O.; Fregly, Benjamin J.; Rice Computational Neuromechanics Laboratory
Introduction: Surgical planning and custom prosthesis design for pelvic cancer patients are challenging due to the unique clinical characteristics of each patient and the significant amount of pelvic bone and hip musculature often removed. Limb-sparing internal hemipelvectomy surgery with custom prosthesis reconstruction has become a viable option for this patient population. However, little is known about how post-surgery walking function and neural control change from pre-surgery conditions. Methods: This case study combined comprehensive walking data (video motion capture, ground reaction, and electromyography) with personalized neuromusculoskeletal computer models to provide a thorough assessment of pre- to post-surgery changes in walking function (ground reactions, joint motions, and joint moments) and neural control (muscle synergies) for a single pelvic sarcoma patient who received internal hemipelvectomy surgery with custom prosthesis reconstruction. Pre- and post-surgery walking function and neural control were quantified using pre- and post-surgery neuromusculoskeletal models, respectively, whose pelvic anatomy, joint functional axes, muscle-tendon properties, and muscle synergy controls were personalized using the participant’s pre-and post-surgery walking and imaging data. For the post-surgery model, virtual surgery was performed to emulate the implemented surgical decisions, including removal of hip muscles and implantation of a custom prosthesis with total hip replacement. Results: The participant’s post-surgery walking function was marked by a slower self-selected walking speed coupled with several compensatory mechanisms necessitated by lost or impaired hip muscle function, while the participant’s post-surgery neural control demonstrated a dramatic change in coordination strategy (as evidenced by modified time-invariant synergy vectors) with little change in recruitment timing (as evidenced by conserved time-varying synergy activations). Furthermore, the participant’s post-surgery muscle activations were fitted accurately using his pre-surgery synergy activations but fitted poorly using his pre-surgery synergy vectors. Discussion: These results provide valuable information about which aspects of post-surgery walking function could potentially be improved through modifications to surgical decisions, custom prosthesis design, or rehabilitation protocol, as well as how computational simulations could be formulated to predict post-surgery walking function reliably given a patient’s pre-surgery walking data and the planned surgical decisions and custom prosthesis design.
Comparison of synergy extrapolation and static optimization for estimating multiple unmeasured muscle activations during walking
(Springer Nature, 2024) Ao, Di; Fregly, Benjamin J.
Calibrated electromyography (EMG)-driven musculoskeletal models can provide insight into internal quantities (e.g., muscle forces) that are difficult or impossible to measure experimentally. However, the need for EMG data from all involved muscles presents a significant barrier to the widespread application of EMG-driven modeling methods. Synergy extrapolation (SynX) is a computational method that can estimate a single missing EMG signal with reasonable accuracy during the EMG-driven model calibration process, yet its performance in estimating a larger number of missing EMG signals remains unknown.
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Computational Design of FastFES Treatment to Improve Propulsive Force Symmetry During Post-stroke Gait: A Feasibility Study
(Frontiers, 2019) Sauder, Nathan R.; Meyer, Andrew J.; Allen, Jessica L.; Ting, Lena H.; Kesar, Trisha M.; Fregly, Benjamin J.
Stroke is a leading cause of long-term disability worldwide and often impairs walking ability. To improve recovery of walking function post-stroke, researchers have investigated the use of treatments such as fast functional electrical stimulation (FastFES). During FastFES treatments, individuals post-stroke walk on a treadmill at their fastest comfortable speed while electrical stimulation is delivered to two muscles of the paretic ankle, ideally to improve paretic leg propulsion and toe clearance. However, muscle selection and stimulation timing are currently standardized based on clinical intuition and a one-size-fits-all approach, which may explain in part why some patients respond to FastFES training while others do not. This study explores how personalized neuromusculoskeletal models could potentially be used to enable individual-specific selection of target muscles and stimulation timing to address unique functional limitations of individual patients post-stroke. Treadmill gait data, including EMG, surface marker positions, and ground reactions, were collected from an individual post-stroke who was a non-responder to FastFES treatment. The patient's gait data were used to personalize key aspects of a full-body neuromusculoskeletal walking model, including lower-body joint functional axes, lower-body muscle force generating properties, deformable foot-ground contact properties, and paretic and non-paretic leg neural control properties. The personalized model was utilized within a direct collocation optimal control framework to reproduce the patient's unstimulated treadmill gait data (verification problem) and to generate three stimulated walking predictions that sought to minimize inter-limb propulsive force asymmetry (prediction problems). The three predictions used: (1) Standard muscle selection (gastrocnemius and tibialis anterior) with standard stimulation timing, (2) Standard muscle selection with optimized stimulation timing, and (3) Optimized muscle selection (soleus and semimembranosus) with optimized stimulation timing. Relative to unstimulated walking, the optimal control problems predicted a 41% reduction in propulsive force asymmetry for scenario (1), a 45% reduction for scenario (2), and a 64% reduction for scenario (3), suggesting that non-standard muscle selection may be superior for this patient. Despite these predicted improvements, kinematic symmetry was not noticeably improved for any of the walking predictions. These results suggest that personalized neuromusculoskeletal models may be able to predict personalized FastFES training prescriptions that could improve propulsive force symmetry, though inclusion of kinematic requirements would be necessary to improve kinematic symmetry as well.
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Computational modeling and simulation of closed chain arm-robot multibody dynamic systems in OpenSim
(Springer Nature, 2022) Green, Matthew; Hong, Yoon No Gregory; Roh, Jinsook; Fregly, Benjamin J.
Rehabilitation robot efficacy for restoring upper extremity function post-stroke could potentially be improved if robot control algorithms accounted for patient-specific neural control deficiencies. As a first step toward the development of such control algorithms using model-based methods, this study provides general guidelines for creating and simulating closed chain arm-robot models in the OpenSim environment, along with a specific example involving a three-dimensional arm moving within a two degree-of-freedom upper extremity rehabilitation robot. The closed chain arm-robot model developed in OpenSim was evaluated using experimental robot motion and torque data collected from a single healthy subject under four conditions: 1) active robot alone, 2) active robot with passive arm, 3) passive robot with active arm, and 4) active robot with active arm. Computational verification of the combined model was performed for all four conditions, whereas experimental validation was performed for only the first two conditions since torque measurements were not available for the arm. For the four verification problems, forward dynamic simulations reproduced experimentally measured robot joint angles with average root-mean-square (RMS) errors of less than 0.3 degrees and correlation coefficients of 1.00. For the two validation problems, inverse dynamic simulations reproduced experimentally measured robot motor torques with average RMS errors less than or equal to 0.5 Nm and correlation coefficients between 0.92 and 0.99. If patient-specific muscle–tendon and neural control models can be successfully added in the future, the coupled arm-robot OpenSim model may provide a useful testbed for designing patient-specific robot control algorithms that facilitate recovery of upper extremity function post-stroke.
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Computational Simulation of Coupled Arm-Robot Motion to Facilitate the Design of Rehabilitation Interventions
(2021-04-29) Green, Matthew John; Fregly, Benjamin J.
Rehabilitation robots have significant potential to facilitate the recovery of lost upper extremity function following stroke. However, they have not produced better functional outcomes than those achieved through conventional therapy, in part because generic robot control algorithms (e.g., assist as needed, error augmentation) do not take into account patient-specific neural control deficiencies. One way to address this limitation is by combining upper extremity neuromusculoskeletal models with rehabilitation robot models, thereby permitting the design of patient-specific robot control algorithms. As a first step toward this goal, this thesis addresses the challenges involved in combining an upper extremity musculoskeletal model with a rehabilitation robot model. The development of the combined arm-robot model consisted of building a model for the two DOF Kinarm rehabilitation robot and coupling that model with a published upper extremity neuromusculoskeletal model. This process was complex due to the many instances of closed kinematic chains, therefore simulations for verification of both developed models and validation against experimental data were a pivotal focus of this study. Experimental data were collected from the Kinarm robot with and without a subject for 12 different planar motions of the arm. Verification was performed on four different configurations of models and controllers: robot model, robot controlled arm-robot model, arm controlled arm-robot model, and cooperative controlled arm-robot model. In addition, both the robot model and combined model were validated against experimental data. All four configurations were verified to reproduce experimental motion with high levels of accuracy and both models were validated to accurately recreate experimental torques with improvement possible the in arm-robot model. The coupled arm-robot model presented in this thesis can serve as the foundation for development of cooperative arm-robot control algorithms.
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Development and Applications of Neuromusculoskeletal Modeling Software for Personalized Treatment Design
(2021-11-29) Vega, Marleny; Fregly, Benjamin J.
One in eight adults with a disability suffer from walking impairments were amputation, osteoarthritis, rheumatoid arthritis, multiple sclerosis, spinal cord injury, stroke, and traumatic brain injury are the most common conditions responsible. Along with other movement impairment conditions such as cerebral palsy, Parkinson's disease, and orthopedic cancer, these conditions have been associated with a decreased quality of life, an increased risk of serious secondary health conditions (e.g., heart disease, diabetes), and an increase in economic burden (e.g., unemployment, health care). Therefore, improving treatment for walking impairment conditions is a high rehabilitation priority and an important public health problem. Clinicians and researchers have explored various neurorehabilitation treatments in search of effective approaches for maximizing walking function recovery. However, personalizing the design and delivery of neurorehabilitation treatments to the needs of individual patients is a challenging data science problem. Although a vast array of disparate movement-related data are available to the clinician, these data have not resulted in highly effective neurorehabilitation treatments. A promising alternative is to base treatment design on objective computational walking models that obey laws of physics and principles of physiology. With this approach, engineering design optimization methods that have successfully transformed the design of airplanes, automobiles, and other products can be used to optimize the design of clinical interventions. For such an approach to work, the computational walking models must be personalized to the patient's unique anatomical, physiological, and neurological characteristics and must be able to predict via optimization the patient's walking function following a planned intervention. Although the necessary computational methods for both capabilities exist today in validated prototype form in Dr. B.J. Fregly's lab at Rice University, they are not packaged in a way that makes them readily accessible and easy to use, thereby preventing significant research progress in this important area. This dissertation 1) developed a software infrastructure, 2) enhanced that infrastructure with metabolic cost modeling, and 3) applied that infrastructure to pelvic sarcoma surgery. We showed that it was possible to develop a cohesive framework to generate personalized neuromusculoskeletal walking models. This framework was further enhanced by adding metabolic cost modeling. We also found that model personalization improved metabolic cost estimates. The entire framework was then used to predict physically realistic post-surgery walking function for a simulated individual with a pelvic sarcoma. Although preserving the psoas muscle increases the surgery time, it is claimed to increase mobility post-surgery and rehabilitation. However, our walking predictions revealed that the strength of this muscle did not have a strong influence on post-surgery walking function. This thesis shows that our current infrastructure has the potential to positively influence surgical or rehabilitative decisions for a wide array of walking impairments.
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Do Muscle Synergies Improve Optimization Prediction of Muscle Activations During Gait?
(Frontiers, 2020) Michaud, Florian; Shourijeh, Mohammad S.; Fregly, Benjamin J.; Cuadrado, Javier; Rice Computational Neuromechanics Laboratory
Determination of muscle forces during motion can help to understand motor control, assess pathological movement, diagnose neuromuscular disorders, or estimate joint loads. Difficulty of in vivo measurement made computational analysis become a common alternative in which, as several muscles serve each degree of freedom, the muscle redundancy problem must be solved. Unlike static optimization (SO), synergy optimization (SynO) couples muscle activations across all time frames, thereby altering estimated muscle co-contraction. This study explores whether the use of a muscle synergy structure within an SO framework improves prediction of muscle activations during walking. A motion/force/electromyography (EMG) gait analysis was performed on five healthy subjects. A musculoskeletal model of the right leg actuated by 43 Hill-type muscles was scaled to each subject and used to calculate joint moments, muscle–tendon kinematics, and moment arms. Muscle activations were then estimated using SynO with two to six synergies and traditional SO, and these estimates were compared with EMG measurements. Synergy optimization neither improved SO prediction of experimental activation patterns nor provided SO exact matching of joint moments. Finally, synergy analysis was performed on SO estimated activations, being found that the reconstructed activations produced poor matching of experimental activations and joint moments. As conclusion, it can be said that, although SynO did not improve prediction of muscle activations during gait, its reduced dimensional control space could be beneficial for applications such as functional electrical stimulation or motion control and prediction.
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EMG-driven musculoskeletal model calibration with estimation of unmeasured muscle excitations via synergy extrapolation
(Frontiers Media S.A., 2022) Ao, Di; Vega, Marleny M.; Shourijeh, Mohammad S.; Patten, Carolynn; Fregly, Benjamin J.; Rice Computational Neuromechanics Lab
Subject-specific electromyography (EMG)-driven musculoskeletal models that predict muscle forces have the potential to enhance our knowledge of internal biomechanics and neural control of normal and pathological movements. However, technical gaps in experimental EMG measurement, such as inaccessibility of deep muscles using surface electrodes or an insufficient number of EMG channels, can cause difficulties in collecting EMG data from muscles that contribute substantially to joint moments, thereby hindering the ability of EMG-driven models to predict muscle forces and joint moments reliably. This study presents a novel computational approach to address the problem of a small number of missing EMG signals during EMG-driven model calibration. The approach (henceforth called “synergy extrapolation” or SynX) linearly combines time-varying synergy excitations extracted from measured muscle excitations to estimate 1) unmeasured muscle excitations and 2) residual muscle excitations added to measured muscle excitations. Time-invariant synergy vector weights defining the contribution of each measured synergy excitation to all unmeasured and residual muscle excitations were calibrated simultaneously with EMG-driven model parameters through a multi-objective optimization. The cost function was formulated as a trade-off between minimizing joint moment tracking errors and minimizing unmeasured and residual muscle activation magnitudes. We developed and evaluated the approach by treating a measured fine wire EMG signal (iliopsoas) as though it were “unmeasured” for walking datasets collected from two individuals post-stroke–one high functioning and one low functioning. How well unmeasured muscle excitations and activations could be predicted with SynX was assessed quantitatively for different combinations of SynX methodological choices, including the number of synergies and categories of variability in unmeasured and residual synergy vector weights across trials. The two best methodological combinations were identified, one for analyzing experimental walking trials used for calibration and another for analyzing experimental walking trials not used for calibration or for predicting new walking motions computationally. Both methodological combinations consistently provided reliable and efficient estimates of unmeasured muscle excitations and activations, muscle forces, and joint moments across both subjects. This approach broadens the possibilities for EMG-driven calibration of muscle-tendon properties in personalized neuromusculoskeletal models and may eventually contribute to the design of personalized treatments for mobility impairments.
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Evaluation of finite element modeling methods for predicting compression screw failure in a custom pelvic implant
(Frontiers Media S.A., 2024) Zhu, Yuhui; Babazadeh-Naseri, Ata; Brake, Matthew R. W.; Akin, John E.; Li, Geng; Lewis, Valerae O.; Fregly, Benjamin J.
Introduction: Three-dimensional (3D)-printed custom pelvic implants have become a clinically viable option for patients undergoing pelvic cancer surgery with resection of the hip joint. However, increased clinical utilization has also necessitated improved implant durability, especially with regard to the compression screws used to secure the implant to remaining pelvic bone. This study evaluated six different finite element (FE) screw modeling methods for predicting compression screw pullout and fatigue failure in a custom pelvic implant secured to bone using nine compression screws. Methods: Three modeling methods (tied constraints (TIE), bolt load with constant force (BL-CF), and bolt load with constant length (BL-CL)) generated screw axial forces using functionality built into Abaqus FE software; while the remaining three modeling methods (isotropic pseudo-thermal field (ISO), orthotropic pseudo-thermal field (ORT), and equal-and-opposite force field (FOR)) generated screw axial forces using iterative physics-based relationships that can be implemented in any FE software. The ability of all six modeling methods to match specified screw pretension forces and predict screw pullout and fatigue failure was evaluated using an FE model of a custom pelvic implant with total hip replacement. The applied hip contact forces in the FE model were estimated at two locations in a gait cycle. For each of the nine screws in the custom implant FE model, likelihood of screw pullout failure was predicted using maximum screw axial force, while likelihood of screw fatigue failure was predicted using maximum von Mises stress. Results: The three iterative physics-based modeling methods and the non-iterative Abaqus BL-CL method produced nearly identical predictions for likelihood of screw pullout and fatigue failure, while the other two built-in Abaqus modeling methods yielded vastly different predictions. However, the Abaqus BL-CL method required the least computation time, largely because an iterative process was not needed to induce specified screw pretension forces. Of the three iterative methods, FOR required the fewest iterations and thus the least computation time. Discussion: These findings suggest that the BL-CL screw modeling method is the best option when Abaqus is used for predicting screw pullout and fatigue failure in custom pelvis prostheses, while the iterative physics-based FOR method is the best option if FE software other than Abaqus is used.
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Evaluation of Optimal Control Approaches for Predicting Active Knee-Ankle-Foot-Orthosis Motion for Individuals With Spinal Cord Injury
(Frontiers Media S.A., 2022) Febrer-Nafría, Míriam; Fregly, Benjamin J.; Font-Llagunes, Josep M.
Gait restoration of individuals with spinal cord injury can be partially achieved using active orthoses or exoskeletons. To improve the walking ability of each patient as much as possible, it is important to personalize the parameters that define the device actuation. This study investigates whether using an optimal control-based predictive simulation approach to personalize pre-defined knee trajectory parameters for an active knee-ankle-foot orthosis (KAFO) used by spinal cord injured (SCI) subjects could potentially be an alternative to the current trial-and-error approach. We aimed to find the knee angle trajectory that produced an improved orthosis-assisted gait pattern compared to the one with passive support (locked knee). We collected experimental data from a healthy subject assisted by crutches and KAFOs (with locked knee and with knee flexion assistance) and from an SCI subject assisted by crutches and KAFOs (with locked knee). First, we compared different cost functions and chose the one that produced results closest to experimental locked knee walking for the healthy subject (angular coordinates mean RMSE was 5.74°). For this subject, we predicted crutch-orthosis-assisted walking imposing a pre-defined knee angle trajectory for different maximum knee flexion parameter values, and results were evaluated against experimental data using that same pre-defined knee flexion trajectories in the real device. Finally, using the selected cost function, gait cycles for different knee flexion assistance were predicted for an SCI subject. We evaluated changes in four clinically relevant parameters: foot clearance, stride length, cadence, and hip flexion ROM. Simulations for different values of maximum knee flexion showed variations of these parameters that were consistent with experimental data for the healthy subject (e.g., foot clearance increased/decreased similarly in experimental and predicted motions) and were reasonable for the SCI subject (e.g., maximum parameter values were found for moderate knee flexion). Although more research is needed before this method can be applied to choose optimal active orthosis controller parameters for specific subjects, these findings suggest that optimal control prediction of crutch-orthosis-assisted walking using biomechanical models might be used in place of the trial-and-error method to select the best maximum knee flexion angle during gait for a specific SCI subject.
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Evaluation of Synergy Extrapolation for Predicting Unmeasured Muscle Excitations from Measured Muscle Synergies
(Frontiers, 2020) Ao, Di; Shourijeh, Mohammad S.; Patten, Carolynn; Fregly, Benjamin J.; Rice Computational Neuromechanics Lab
Electromyography (EMG)-driven musculoskeletal modeling relies on high-quality measurements of muscle electrical activity to estimate muscle forces. However, a critical challenge for practical deployment of this approach is missing EMG data from muscles that contribute substantially to joint moments. This situation may arise due to either the inability to measure deep muscles with surface electrodes or the lack of a sufficient number of EMG channels. Muscle synergy analysis (MSA) is a dimensionality reduction approach that decomposes a large number of muscle excitations into a small number of time-varying synergy excitations along with time-invariant synergy weights that define the contribution of each synergy excitation to all muscle excitations. This study evaluates how well missing muscle excitations can be predicted using synergy excitations extracted from muscles with available EMG data (henceforth called “synergy extrapolation” or SynX). The method was evaluated using a gait data set collected from a stroke survivor walking on an instrumented treadmill at self-selected and fastest-comfortable speeds. The evaluation process started with full calibration of a lower-body EMG-driven model using 16 measured EMG channels (collected using surface and fine wire electrodes) per leg. One fine wire EMG channel (either iliopsoas or adductor longus) was then treated as unmeasured. The synergy weights associated with the unmeasured muscle excitation were predicted by solving a nonlinear optimization problem where the errors between inverse dynamics and EMG-driven joint moments were minimized. The prediction process was performed for different synergy analysis algorithms (principal component analysis and non-negative matrix factorization), EMG normalization methods, and numbers of synergies. SynX performance was most influenced by the choice of synergy analysis algorithm and number of synergies. Principal component analysis with five or six synergies consistently predicted unmeasured muscle excitations the most accurately and with the greatest robustness to EMG normalization method. Furthermore, the associated joint moment matching accuracy was comparable to that produced by initial EMG-driven model calibration using all 16 EMG channels per leg. SynX may facilitate the assessment of human neuromuscular control and biomechanics when important EMG signals are missing.
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Finite element analysis of screw fixation durability under multiple boundary and loading conditions for a custom pelvic implant
(Elsevier, 2023) Zhu, Yuhui; Babazadeh-Naseri, Ata; Dunbar, Nicholas J.; Brake, Matthew R.W.; Zandiyeh, Payam; Li, Geng; Leardini, Alberto; Spazzoli, Benedetta; Fregly, Benjamin J.
Despite showing promising functional outcomes for pelvic reconstruction after sarcoma resection, custom-made pelvic implants continue to exhibit high complication rates due to fixation failures. Patient-specific finite element models have been utilized by researchers to evaluate implant durability. However, the effect of assumed boundary and loading conditions on failure analysis results of fixation screws remains unknown. In this study, the postoperative stress distributions in the fixation screws of a state-of-the-art custom-made pelvic implant were simulated, and the risk of failure was estimated under various combinations of two bone-implant interaction models (tied vs. frictional contact) and four load cases from level-ground walking and stair activities. The study found that the average weighted peak von Mises stress could increase by 22-fold when the bone-implant interactions were modeled with a frictional contact model instead of a tied model, and the likelihood of fatigue and pullout failure for each screw could change dramatically when different combinations of boundary and loading conditions were used. The inclusion of additional boundary and loading conditions led to a more reliable analysis of fixation durability. These findings demonstrated the importance of simulating multiple boundary conditions and load cases for comprehensive implant design evaluation using finite element analysis.
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Heterogeneous material mapping methods for patient-specific finite element models of pelvic trabecular bone: A convergence study
(Elsevier, 2021) Babazadeh Naseri, Ata; Dunbar, Nicholas J.; Baines, Andrew J.; Akin, John E.; Higgs, C. Fred III; Fregly, Benjamin J.
Patient-specific finite element (FE) models of bone require the assignment of heterogeneous material properties extracted from the subject's computed tomography (CT) images. Though node-based (NB) and element-based (EB) material mapping methods (MMMs) have been proposed, the sensitivity and convergence of FE models to MMM for varying mesh sizes are not well understood. In this work, CT-derived and synthetic bone material data were used to evaluate the effect of MMM on results from FE analyses. Pelvic trabecular bone data was extracted from CT images of six subjects, while synthetic data were created to resemble trabecular bone properties. The numerical convergence of FE bone models using different MMMs were evaluated for strain energy, von-Mises stress, and strain. NB and EB MMMs both demonstrated good convergence regarding total strain energy, with the EB method having a slight edge over the NB. However, at the local level (e.g., maximum stress and strain), FE results were sensitive to the field type, MMM, and the FE mesh size. The EB method exhibited superior performance in finer meshes relative to the voxel size. The NB method converged better than did the EB method for coarser meshes. These findings may lead to higher-fidelity patient-specific FE bone models.
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How Well Do Commonly Used Co-contraction Indices Approximate Lower Limb Joint Stiffness Trends During Gait for Individuals Post-stroke?
(Frontiers, 2021) Li, Geng; Shourijeh, Mohammad S.; Ao, Di; Patten, Carolynn; Fregly, Benjamin J.; Rice Computational Neuromechanics Laboratory
Muscle co-contraction generates joint stiffness to improve stability and accuracy during limb movement but at the expense of higher energetic cost. However, quantification of joint stiffness is difficult using either experimental or computational means. In contrast, quantification of muscle co-contraction using an EMG-based Co-Contraction Index (CCI) is easier and may offer an alternative for estimating joint stiffness. This study investigated the feasibility of using two common CCI’s to approximate lower limb joint stiffness trends during gait. Calibrated EMG-driven lower extremity musculoskeletal models constructed for two individuals post-stroke were used to generate the quantities required for CCI calculations and model-based estimation of joint stiffness. CCIs were calculated for various combinations of antagonist muscle pairs based on two common CCI formulations: Rudolph et al. (2000) (CCI1) and Falconer and Winter (1985) (CCI2). CCI1 measures antagonist muscle activation relative to not only total activation of agonist plus antagonist muscles but also agonist muscle activation, while CCI2 measures antagonist muscle activation relative to only total muscle activation. We computed the correlation between these two CCIs and model-based estimates of sagittal plane joint stiffness for the hip, knee, and ankle of both legs. Although we observed moderate to strong correlations between some CCI formulations and corresponding joint stiffness, these associations were highly dependent on the methodological choices made for CCI computation. Specifically, we found that: (1) CCI1 was generally more correlated with joint stiffness than was CCI2, (2) CCI calculation using EMG signals with calibrated electromechanical delay generally yielded the best correlations with joint stiffness, and (3) choice of antagonist muscle pairs significantly influenced CCI correlation with joint stiffness. By providing guidance on how methodological choices influence CCI correlation with joint stiffness trends, this study may facilitate a simpler alternate approach for studying joint stiffness during human movement.
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Improving Computational Fixation Durability Evaluation for Custom-made Pelvic Implants Using Physiological Boundary and Loading Conditions
(2023-11-30) Zhu, Maggie; Fregly, Benjamin J.
The treatment for pelvic sarcoma often involves complete resection of the tumor and results in a sizable bone defect in the pelvis. Recently, reconstruction of the resulting bone defect with a 3D-printed custom-made implant has become increasingly popular because of the promising functional outcomes it can provide. However, the typical design process of a custom implant today lacks an engineering assessment of the implant’s durability. As a result, complications due to structural failures, such as fixation failures, remain high. Breakage or pullout failures of fixation screws are common, sometimes necessitating surgical revisions. With adequate design verifications, fixation failures may be adverted. A few studies have incorporated patient-specific finite element models into the implant design process, in an attempt to detect potential structural failures of the postoperative pelvic construct. However, these finite element models are usually subject to arbitrary modeling choices, casting doubts about the reliability of these models. To date, no standard for constructing these finite element models has been proposed and the effect of many modeling choices on the predicted fixation durability remains unexplored. This thesis presents a series of investigations of the effect of various boundary and loading conditions on computational fixation durability evaluation using patient-specific finite element models. The investigations aimed to 1) examine the accuracy of the common modeling practices, 2) propose modeling techniques that better reflect the physiological relationships within the postoperative pelvis, and 3) improve the current framework of constructing the finite element model for fixation durability evaluation. One by one, four distinct aspects of the boundary and loading conditions were studied by addressing the unique modeling challenges posed by two different implant designs – 1) the interactions between the remaining bone and implant, 2) the variety of the hip joint contact forces, 3) the modeling techniques of two distinct categories of orthopedic fixation screws, and 4) the inclusion of muscle forces. First, we established that the computational fixation durability was sensitive to the imposed boundary and loading conditions. The predicted likelihood for screw failure was more conservative when boundary conditions reflected early-stage osseointegration or loading conditions reflecting a wide range of daily activities were applied. Second, we explored the importance of choosing a physiological screw model for predicting screw failures and proposed a novel method of modeling compressive screws. The proposed method made assessing pullout failures for compressive screws possible. Lastly, we implemented all the previous improvements of the model and incorporated muscle forces into the finite element model. This patient-specific finite element model was subject to not only physiological boundary conditions but also postoperative muscle and hip joint contact forces which were predicted with a personalized neuromusculoskeletal model. We found the inclusion of muscle forces had a greater influence on pullout failure evaluation than breakage failure evaluation. Through these investigations, this thesis demonstrated the importance of carefully selecting physiological boundary and loading conditions for analyzing the durability of the screws used to fixate custom pelvic implants. The analyses of the modeling practices introduced in the improved model were crucial steps for gaining confidence in the computational evaluation framework within the pelvic implant design community and for providing a higher standard of care for pelvic sarcoma patients.
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Inclusion of Muscle Forces Affects Finite Element Prediction of Compression Screw Pullout but Not Fatigue Failure in a Custom Pelvic Implant
(MDPI, 2024) Zhu, Yuhui; Babazadeh-Naseri, Ata; Brake, Matthew R. W.; Akin, John E.; Li, Geng; Lewis, Valerae O.; Fregly, Benjamin J.
Custom implants used for pelvic reconstruction in pelvic sarcoma surgery face a high complication rate due to mechanical failures of fixation screws. Consequently, patient-specific finite element (FE) models have been employed to analyze custom pelvic implant durability. However, muscle forces have often been omitted from FE studies of the post-operative pelvis with a custom implant, despite the lack of evidence that this omission has minimal impact on predicted bone, implant, and fixation screw stress distributions. This study investigated the influence of muscle forces on FE predictions of fixation screw pullout and fatigue failure in a custom pelvic implant. Specifically, FE analyses were conducted using a patient-specific FE model loaded with seven sets of personalized muscle and hip joint contact force loading conditions estimated using a personalized neuromusculoskeletal (NMS) model. Predictions of fixation screw pullout and fatigue failure—quantified by simulated screw axial forces and von Mises stresses, respectively—were compared between analyses with and without personalized muscle forces. The study found that muscle forces had a considerable influence on predicted screw pullout but not fatigue failure. However, it remains unclear whether including or excluding muscle forces would yield more conservative predictions of screw failures. Furthermore, while the effect of muscle forces on predicted screw failures was location-dependent for cortical screws, no clear location dependency was observed for cancellous screws. These findings support the combined use of patient-specific FE and NMS models, including loading from muscle forces, when predicting screw pullout but not fatigue failure in custom pelvic implants.
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Influence of musculoskeletal model parameter values on prediction of accurate knee contact forces during walking
(Elsevier, 2020) Serrancolí, Gil; Kinney, Allison L.; Fregly, Benjamin J.
Treatment design for musculoskeletal disorders using in silico patient-specific dynamic simulations is becoming a clinical possibility. However, these simulations are sensitive to model parameter values that are difficult to measure experimentally, and the influence of uncertainties in these parameter values on the accuracy of estimated knee contact forces remains unknown. This study evaluates which musculoskeletal model parameters have the greatest influence on estimating accurate knee contact forces during walking. We performed the evaluation using a two-level optimization algorithm where musculoskeletal model parameter values were adjusted in the outer level and muscle activations were estimated in the inner level. We tested the algorithm with different sets of design variables (combinations of optimal muscle fiber lengths, tendon slack lengths, and muscle moment arm offsets) resulting in nine different optimization problems. The most accurate lateral knee contact force predictions were obtained when tendon slack lengths and moment arm offsets were adjusted simultaneously, and the most accurate medial knee contact force estimations were obtained when all three types of parameters were adjusted together. Inclusion of moment arm offsets as design variables was more important than including either tendon slack lengths or optimal muscle fiber lengths alone to obtain accurate medial and lateral knee contact force predictions. These results provide guidance on which musculoskeletal model parameter values should be calibrated when seeking to predict in vivo knee contact forces accurately.
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Musculoskeletal Model Personalization Affects Metabolic Cost Estimates for Walking
(Frontiers, 2020) Arones, Marleny M.; Shourijeh, Mohammad S.; Patten, Carolynn; Fregly, Benjamin J.
Assessment of metabolic cost as a metric for human performance has expanded across various fields within the scientific, clinical, and engineering communities. As an alternative to measuring metabolic cost experimentally, musculoskeletal models incorporating metabolic cost models have been developed. However, to utilize these models for practical applications, the accuracy of their metabolic cost predictions requires improvement. Previous studies have reported the benefits of using personalized musculoskeletal models for various applications, yet no study has evaluated how model personalization affects metabolic cost estimation. This study investigated the effect of musculoskeletal model personalization on estimates of metabolic cost of transport (CoT) during post-stroke walking using three commonly used metabolic cost models. We analyzed walking data previously collected from two male stroke survivors with right-sided hemiparesis. The three metabolic cost models were implemented within three musculoskeletal modeling approaches involving different levels of personalization. The first approach used a scaled generic OpenSim model and found muscle activations via static optimization (SOGen). The second approach used a personalized electromyographic (EMG)-driven musculoskeletal model with personalized functional axes but found muscle activations via static optimization (SOCal). The third approach used the same personalized EMG-driven model but calculated muscle activations directly from EMG data (EMGCal). For each approach, the muscle activation estimates were used to calculate each subject’s CoT at different gait speeds using three metabolic cost models (Umberger et al., 2003; Bhargava et al., 2004; Umberger, 2010). The calculated CoT values were compared with published CoT data as a function of walking speed, step length asymmetry, stance time asymmetry, double support time asymmetry, and severity of motor impairment (i.e., Fugl-Meyer score). Overall, only SOCal and EMGCal with the Bhargava metabolic cost model were able to reproduce accurately published experimental trends between CoT and various clinical measures of walking asymmetry post-stroke. Tuning of the parameters in the different metabolic cost models could potentially resolve the observed CoT magnitude differences between model predictions and experimental measurements. Realistic CoT predictions may allow researchers to predict human performance, surgical outcomes, and rehabilitation outcomes reliably using computational simulations.

Browsing by Author "Fregly, Benjamin J."

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