Improving Personalization of Neuromusculoskeletal Models via EMG-driven Model Calibration
| dc.contributor.advisor | Fregly, Benjamin | |
| dc.creator | Ao, Di | |
| dc.date.accessioned | 2022-09-23T20:49:34Z | |
| dc.date.available | 2022-11-01T05:01:16Z | |
| dc.date.created | 2022-05 | |
| dc.date.issued | 2022-04-20 | |
| dc.date.submitted | May 2022 | |
| dc.date.updated | 2022-09-23T20:49:34Z | |
| dc.description.abstract | Neuromusculoskeletal disorders, which have high rates of prevalence and incidence, cause long-term disability. These individuals require substantial assistance in self-care, mobility, and house-hold activities over a long period of time, which places heavy healthcare and economic burdens on individuals, families and society. Surgical and rehabilitation treatments for neuromusculoskeletal disorders are typically subjective and consist of a standard menu of treatment options established using clinicians’ collective experience and judgment. Personalized treatment planning of neuromusculoskeletal fullest level of personalization, such models could facilitate objectivity and agreement between clinicians on the most effective treatment plan for a given individual. Electromyography (EMG)-driven musculoskeletal modeling is an emerging approach to identify subject-specific muscle force generating properties. Once identified, this modeling approach uses these properties to predict EMG-consistent muscle forces despite the muscle redundancy problem underlying musculoskeletal systems. Two critical issues with the practical deployment of EMG-driven modeling approach have received limited attention, both of which generally arise from low-quality model input variables. The first issue is missing EMG data from muscles that contribute substantially to joint moments, primarily due to inherent limitations of surface EMG recording and constraints arising from experimental conditions. The second issue is lack of subject-specific anatomical details in muscle-tendon geometrics, especially in the sizing and placement of simplified surface geometries that are employed to estimate muscle wrapping behavior over joint capsules, bony prominences, muscles of deeper layers, and neighboring soft tissues. This dissertation aims to improve EMG-driven musculoskeletal modeling methods by addressing two important challenges. First, a novel framework is presented to address the problem with EMG-driven model calibration that arises from missing EMG data of important muscles. An approach to estimate unmeasured muscle excitations using information extracted from measured muscle excitations (termed “synergy extrapolation” or “SynX”) was developed within the context of EMG-driven modeling. The development process started with the evaluation of SynX performance for different methodological combinations when EMG-driven model parameters were well-calibrated. Then, a multi-objective optimization problem was formulated that allowed SynX- predicted missing muscle excitations and EMG-driven model parameter values to be identified simultaneously. Two SynX methodological combinations, one targeted at analyzing experimentally measured motions and the other at generating computationally predicted motions, were identified. Both combinations were able to consistently provide accurate unmeasured muscle excitations and reliable muscle force estimates. Second, a novel workflow is presented to increase subject-specificity of muscle wrapping surface geometries via EMG-driven model calibration. The workflow started with the development of a two-level surrogate model of musculoskeletal geometry that could accurately approximate muscle-tendon geometries as functions of joint kinematics and muscle wrapping surface parameters for each muscle in the model. Then, these surrogate musculoskeletal geometric models were incorporated into the EMG-driven modeling process, which allowed muscle wrapping surface parameter to be adjusted through non-linear optimization. The capacity of the EMG-driven model to predict joint moments was significantly enhanced by the personalization of muscle wrapping surfaces, most notably by lowering the magnitude of joint contact force estimates. This dissertation presents two augmented EMG-driven modeling methods that show extensive potential in assessment of human neuromuscular control and biomechanics for optimal treatment design. Disorders is a new approach where an individuals’ specific characteristics are taken into account to restore lost function. Computational neuromusculoskeletal modeling is a powerful tool for providing personalized treatment planning for neuromusculoskeletal disorders. They can not only generate accurate and reliable estimates of important internal biomechanical variables during experimentally measured motions, but also predict objective functional outcomes for a variety of possible interventions. Every individual has unique anatomical and physiological characteristics of their neuromusculoskeletal system which can affect the accuracy of computational predictions. Therefore, personalized neuromusculoskeletal models that take into account these differences would provide more accurate outcome predictions. When employed to their | |
| dc.embargo.terms | 2022-11-01 | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Ao, Di. "Improving Personalization of Neuromusculoskeletal Models via EMG-driven Model Calibration." (2022) Diss., Rice University. <a href="https://hdl.handle.net/1911/113306">https://hdl.handle.net/1911/113306</a>. | |
| dc.identifier.uri | https://hdl.handle.net/1911/113306 | |
| dc.language.iso | eng | |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | |
| dc.subject | neuromusculoskeletal modeling | |
| dc.subject | Electromyography | |
| dc.subject | EMG-driven model calibration | |
| dc.subject | biomechanics | |
| dc.subject | muscle wrapping surface | |
| dc.subject | muscle excitation | |
| dc.title | Improving Personalization of Neuromusculoskeletal Models via EMG-driven Model Calibration | |
| dc.type | Thesis | |
| dc.type.material | Text | |
| thesis.degree.department | Mechanical Engineering | |
| thesis.degree.discipline | Engineering | |
| thesis.degree.grantor | Rice University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |
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