Browsing by Author "Li, Geng"

Now showing 1 - 7 of 7
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    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.
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    Accelerating multielectron reduction at CuxO nanograins interfaces with controlled local electric field
    (Springer Nature, 2023) Guo, Weihua; Zhang, Siwei; Zhang, Junjie; Wu, Haoran; Ma, Yangbo; Song, Yun; Cheng, Le; Chang, Liang; Li, Geng; Liu, Yong; Wei, Guodan; Gan, Lin; Zhu, Minghui; Xi, Shibo; Wang, Xue; Yakobson, Boris I.; Tang, Ben Zhong; Ye, Ruquan
    Regulating electron transport rate and ion concentrations in the local microenvironment of active site can overcome the slow kinetics and unfavorable thermodynamics of CO2 electroreduction. However, simultaneous optimization of both kinetics and thermodynamics is hindered by synthetic constraints and poor mechanistic understanding. Here we leverage laser-assisted manufacturing for synthesizing CuxO bipyramids with controlled tip angles and abundant nanograins, and elucidate the mechanism of the relationship between electron transport/ion concentrations and electrocatalytic performance. Potassium/OH− adsorption tests and finite element simulations corroborate the contributions from strong electric field at the sharp tip. In situ Fourier transform infrared spectrometry and differential electrochemical mass spectrometry unveil the dynamic evolution of critical *CO/*OCCOH intermediates and product profiles, complemented with theoretical calculations that elucidate the thermodynamic contributions from improved coupling at the Cu+/Cu2+ interfaces. Through modulating the electron transport and ion concentrations, we achieve high Faradaic efficiency of 81% at ~900 mA cm−2 for C2+ products via CO2RR. Similar enhancement is also observed for nitrate reduction reaction (NITRR), achieving 81.83 mg h−1 ammonia yield rate per milligram catalyst. Coupling the CO2RR and NITRR systems demonstrates the potential for valorizing flue gases and nitrate wastes, which suggests a practical approach for carbon-nitrogen cycling.
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    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.
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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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    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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    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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    Predicting Post-surgery Walking Function of Pelvis Sarcoma Patients using Personalized Neuromusculoskeletal Models
    (2023-12-01) Li, Geng; Fregly, Benjamin J
    Surgical treatments for pelvic sarcomas have been historically challenging due to the complex anatomy of the pelvic region and large heterogeneity in tumor conditions across patients. Orthopedic oncologists and implant companies must therefore make many decisions in the design of the surgical treatments and the prostheses to address these challenges. However, most of these decisions were still made based on subjective judgments, which raised the question of whether a more objective decision-making approach can be used to further improve the patients’ walking function post-surgery. Predictive simulations of human movement using personalized neuromusculoskeletal models is one approach to design interventions. Surgeons and biomedical engineers can use this approach to generate exhaustive set of treatment designs and objectively evaluate all of them before deciding on the best option. Before the great potential of clinical translation of this technology in treating pelvic sarcomas can be fully realized, it is important to make sure the predictive simulations can reproduce reality at first. This dissertation seeks to use the three technical chapters to methodically address the technical challenges associated with generating realistic model-based predictive simulations. In the first technical chapter (Chapter 2), a personalized musculoskeletal model was created to simulate the gait movement of a pelvic sarcoma patient. The model met our need for being capable of independently simulating movement actuated by both their trunk muscles and lower extremity muscles, since most existing models were usually specialized for musculoskeletal system in only one region of the body e.g. trunk only or leg only. In addition to detailing model development, this technical chapter also addressed the practical problem where insufficient electromyography channels were available to record muscle activities of both trunk and leg muscles. The proposed computational approach would use muscle synergies extracted from the measured activity of leg muscles to provide realistic estimates of trunk muscle activities. In the second technical chapter (Chapter 3), experimental gait data of a pelvic sarcoma patient pre- and post-surgery were collected, analyzed, and compared to provide an understanding on how neural controls of the lower extremity muscles changed after the surgery. Three hypotheses about how partial information of pre-surgery neural controls could be used in predict post-surgery muscle activities (1. Fixed SynVec, pre-surgery synergy vectors were retained, 2. Fixed SynCmd, pre-surgery synergy commands were retained, and 3. Shifted SynCmd, pre-surgery synergy commands were retained but allowed to shift in time) were evaluated. The Fixed SynCmd and Shifted SynCmd hypotheses accurately reconstructed post-surgery muscle activities. In the third technical chapter (Chapter 4), optimizations based on four different assumptions about optimality principles for predicting post-surgery walking were formulated and evaluated. The first proposed minimization of changes in muscle synergies from pre-surgery models. The second proposed minimization of deviations in synergy vector weights from set values. The third proposed minimization of changes in muscle activations from pre-surgery activations, and the fourth proposed minimization of muscle activations during post-surgery walking. The optimization based on minimization of changes in muscle synergies most accurately predicted the muscle activations during post-surgery walking. The research reported in this dissertation establishes the foundation for future works in model-based prediction of post-surgery gait of pelvic sarcoma patients. As more experimental and clinical data are collected, more analyses can be performed and more predictive simulations can be generated using the methods develop in this dissertation, to shed more lights on the appropriate methods to use for predicting post-surgery walking.