1,721,049 research outputs found
CEINMS: A toolbox to investigate the influence of different neural control solutions on the prediction of muscle excitation and joint moments during dynamic motor tasks
Full text linkPersonalized neuromusculoskeletal (NMS) models can represent the neurological, physiological, and anatomical characteristics of an individual and can be used to estimate the forces generated inside the human body. Currently, publicly available software to calculate muscle forces are restricted to static and dynamic optimisation methods, or limited to isometric tasks only. We have created and made freely available for the research community the Calibrated EMG-Informed NMS Modelling Toolbox (CEINMS), an OpenSim plug-in that enables investigators to predict different neural control solutions for the same musculoskeletal geometry and measured movements. CEINMS comprises EMG-driven and EMG-informed algorithms that have been previously published and tested. It operates on dynamic skeletal models possessing any number of degrees of freedom and musculotendon units and can be calibrated to the individual to predict measured joint moments and EMG patterns. In this paper we describe the components of CEINMS and its integration with OpenSim. We then analyse how EMG-driven, EMG-assisted, and static optimisation neural control solutions affect the estimated joint moments, muscle forces, and muscle excitations, including muscle co-contraction
Evaluation of an Electromyography (EMG)-driven Upper Extremity Model for Neurorehabilitation Applications
No full textUpper 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
Predicting Post-surgery Walking Function of Pelvis Sarcoma Patients using Personalized Neuromusculoskeletal Models
No full textSurgical 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
Computational Simulation of Coupled Arm-Robot Motion to Facilitate the Design of Rehabilitation Interventions
No full textRehabilitation 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
Implementation and Use of the Neuromusculoskeletal Modeling Pipeline
No full textNeuromusculoskeletal 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 modelin
Development and Applications of Neuromusculoskeletal Modeling Software for Personalized Treatment Design
No full textOne 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
Improving Computational Fixation Durability Evaluation for Custom-made Pelvic Implants Using Physiological Boundary and Loading Conditions
No full textEMBARGO NOTE: This item is embargoed until 2025-12-01The 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
A Comparison of Computational Muscle Models using Intramuscular Pressure - A Surrogate for Muscle Force
No full textNeuromusculoskeletal (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
An Integrationless Optimization Method for IMU-based Human Motion Measurement
No full textIdeally, 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
Design, Characterization, and Modeling of the MAHI Open Exo
No full textRehabilitation 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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