Browsing by Author "Fregly, Benjamin J"

Now showing 1 - 6 of 6
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    A Comparison of Computational Muscle Models using Intramuscular Pressure - A Surrogate for Muscle Force
    (2019-04-19) Boggess, Grant Forrest; Fregly, Benjamin J
    Neuromusculoskeletal (NM) models may help clinicians design better rehabilitation protocols. However, NM models face two challenges. Firstly, NM models greatly simplify the neuromusculoskeletal system. Secondly, the muscle redundancy problem guarantees there are not unique muscle force solutions, making validation of model changes very challenging. Intramuscular pressure (IMP) is the interstitial fluid pressure in muscle and has been shown to have a high correlation with muscle force. This thesis uses a newly-developed IMP sensor to compare the correlation between IMP and muscle force for four muscle models. Correlations were calculated between predicted tibialis anterior force and IMP for seven ankle dorsiflexion/plantarflexion tasks. Muscle model did not significantly affect the correlation between predicted muscle force and IMP. However, the compliant tendon model did have an insignificant increase in joint moment prediction accuracy. This may indicate that a compliant tendon model is appropriate for strong plantarflexor muscles with long tendons.
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    An Integrationless Optimization Method for IMU-based Human Motion Measurement
    (2022-08-12) Bhateja, Andy S; Fregly, Benjamin J
    Ideally, rehabilitation of neuromusculoskeletal impairments would involve repeated measurement of a patient’s movement capabilities and limitations, facilitating patient assessment throughout the treatment process. While optical motion capture systems are currently the most commonly used technology for measuring human movement, they are expensive and require a well-controlled indoor test environment, necessitating repeated patient visits to the clinic. Wearable inertial measurement units (IMUs) are a cheaper alternative that can measure human movement in any environment, but the state estimation methods commonly used to convert IMU measurements into joint kinematic data require numerical integration of noisy IMU data, resulting in significant integration drift. This study presents a novel integrationless method for measuring human movement with IMUs. The method must be used off-line and employs nonlinear optimization for state estimation, utilizes a physics-based kinematic model with joint constraints to provide the necessary theoretical relationships between IMU kinematics and joint kinematics, estimates joint positions, velocities, and accelerations simultaneously, and replaces numerical integration applied sequentially one time frame at a time with numerical differentiation applied over all time frames simultaneously. The method does not require IMU magnetometer data, calculation of IMU orientation in the global reference frame from IMU gyroscope data, or subtraction of the acceleration due to gravity from IMU accelerometer data. As an enhancement, the method also uses machine learning models to inform estimation of secondary joint kinematics that are not well defined by physics-based relationships alone. The method was evaluated quantitatively using experimental IMU and optical motion capture data collected simultaneously from the pelvis and lower limbs of a single healthy subject who performed walking, jogging, and jumping trials, where inverse kinematic results generated using the optical motion capture data were treated as the “gold standard” joint angle measurements. Without the machine learning enhancement, the proposed integrationless optimization method produced average root-mean-square (RMS) errors on the order of 3 degrees for walking, 6 degrees for jogging, and 12 degrees for jumping. With the machine learning enhancement, these errors were reduced to roughly 3 degrees for all three movements. In contrast, a standard unscented filter method produced average RMS errors of 18 degrees, 19 degrees, and 16 degrees for the same three movements, respectively. These findings suggest that the proposed integrationless optimization method for estimating joint kinematics from IMU data could potentially be used in place of an optical motion capture system for patient assessment situations where real-time measurement capability is not required.
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    Design, Characterization, and Modeling of the MAHI Open Exo
    (2022-04-20) Berning, Jeff Thomas; O'Malley, Marcia K; Fregly, Benjamin J
    Rehabilitation robots provide many theoretical benefits to augment the role of a physical therapist; however, to date, therapeutic outcomes following stroke and spinal cord injury have not been improved with the use of rehabilitation robots. Personalized neuromusculoskeletal models have been developed to model dynamic motion and control of the human body, and the state-of-the-art models are capable of including impairment in the model. Incorporating a dynamic model of a rehabilitation robot working in concert with the human limb would enhance the impact of such models in designing personalized treatments. To realize this, the dynamic model of the robot must be solvable in real-time. These combined models can then be used to create personalized, model-based control strategies with the goal of improving therapeutic outcomes through higher subject engagement following spinal cord injury or stroke. To address this need, this thesis describes the design of the MAHI Open Exoskeleton (MOE), a four degree of freedom, serial exoskeleton device for the upper-limb. A dynamic model of the MAHI Open Exo is presented, along with the characterization and friction modeling of the device. The dynamic model provides the basis for a future human-robot combined model, which will be used for personalized, model- based control strategies.
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    Evaluation of an Electromyography (EMG)-driven Upper Extremity Model for Neurorehabilitation Applications
    (2022-04-21) Ford, Johnathan William; Fregly, Benjamin J
    Upper extremity EMG-driven models have the potential to inform the design of rehabilitation treatments. However, limitations exist when not all muscles have electromyographic (EMG) data available. Therefore, a synergy-based optimization approach was implemented to predict joint moments reliably despite missing EMG signals. Improvements are still needed, but progress is being made towards reliable prediction.
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    Implementation and Use of the Neuromusculoskeletal Modeling Pipeline
    (2024-12-02) Hammond, Claire Vivian; Fregly, Benjamin J
    Neuromusculoskeletal injuries including osteoarthritis, stroke, spinal cord injury, and traumatic brain injury affect roughly 19% of the U.S. adult population. Standardized interventions have produced suboptimal functional outcomes due to the unique treatment needs of each patient. Strides have been made to utilize computational models to develop personalized treatments, but researchers and clinicians have yet to cross the “valley of death” between fundamental research and clinical usefulness. This article introduces the Neuromusculoskeletal Modeling (NMSM) Pipeline, two MATLAB-based toolsets that add Model Personalization and Treatment Optimization functionality to OpenSim. The two toolsets facilitate computational design of individualized treatments for neuromusculoskeletal impairments through the use of personalized neuromusculoskeletal models and predictive simulation. The Model Personalization toolset contains four tools for personalizing 1) joint structure models, 2) muscle-tendon models, 3) neural control models, and 4) foot-ground contact models. The Treatment Optimization toolset contains three tools for predicting and optimizing a patient’s functional outcome for different treatment options using a patient’s personalized neuromusculoskeletal model with direct collocation optimal control methods. Two NMSM Pipeline use cases are presented. The first example is an individual post-stroke with impaired walking function, where the goal is to predict how the subject’s neural control could be changed to improve walking speed without increasing metabolic cost. First the Model Personalization toolset was used to develop a personalized neuromusculoskeletal model of the subject starting from a generic OpenSim full-body model and experimental walking data (video motion capture, ground reaction, and electromyography) collected from the subject at his self-selected speed. Next the Treatment Optimization toolset was used with the personalized model to predict how the subject could recruit existing muscle synergies more effectively to reduce muscle activation disparities between the paretic and non-paretic legs. The software predicted that the subject could increase his walking speed by 60% without increasing his metabolic cost per unit time by modifying the recruitment of his existing muscle synergies. This hypothetical treatment demonstrates how NMSM Pipeline tools could allow researchers working collaboratively with clinicians to develop personalized neuromusculoskeletal models of individual patients and to perform predictive simulations for the purpose of designing personalized treatments that maximize a patient’s post-treatment functional outcome. The second example is a novel personalized closed-chain kinematic shoulder model creation process utilizing the first Model Personalization tool, Joint Model Personalization. Commonly used kinematic shoulder models typically use regression-based kinematics and open-chain constructions, these models can produce low accuracy and anatomically impossible kinematics for many motions and subjects. After creating synthetic marker data and a model compatible with the NMSM Pipeline, joint parameters were automatically optimized to minimize the error between modeled kinematics and experimental kinematics of eight motions. The software produced a series of models with average marker distance errors below 1 millimeter across all motions for the best 5 degree of freedom model. This novel personalized closed-chain kinematic shoulder illustrates the ability of the NMSM Pipeline to influence the field of neuromusculoskeletal modeling
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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.