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Full text release has been delayed at the author's request until May 17, 2025

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Bayesian Analysis of Muscle Recruitment Patterns in Locomotion

Amankwah, Mercy Gyamea
http://orcid.org/0000-0002-5340-7782
http://rave.ohiolink.edu/etdc/view?acc_num=case1711387309757226

Abstract Details

2024, Doctor of Philosophy, Case Western Reserve University, Applied Mathematics.
This doctoral dissertation is concerned with solving the inverse problem of human movement in terms of muscle forces and activation. While feasible to measure some of these forces directly in the human body through invasive procedures, the inverse problem of musculoskeletal modeling can estimate these forces and activation non-invasively, thus presenting a safer and more practical alternative once estimates are thoroughly validated. In this thesis, we set up the muscle recruitment problem as a Bayesian inverse problem, and we estimate muscle forces and activations while concurrently quantifying the associated uncertainties. The abundance of muscles relative to available degrees of freedom grants the human musculoskeletal system redundancy, enabling diverse muscle activation patterns during motor tasks. This redundancy is crucial for the system's functionality across various conditions, including pathological states. A fundamental challenge in biomechanics involves understanding how the complex interaction between the central nervous system and musculoskeletal system, characterized by redundancy, governs normal activation patterns and their evolution in abnormal conditions, such as neurodegenerative diseases and aging. This dissertation presents a mathematical framework to address this challenge through Bayesian probabilistic modeling of the musculoskeletal system. Using Lagrangian dynamics, observed movements are transformed into time series of equlibria that constitute the basis of the likelihood model. Various prior models, aligned with biologically inspired assumptions regarding muscle dynamics and control, are introduced and tested. The corresponding posterior distributions of muscle activations are explored using Markov chain Monte Carlo (MCMC) sampling techniques. The different priors are evaluated by comparing the model predictions with actual observations. This thesis also proposes a model for sparse muscle recruitment that could be of use in scenarios where certain muscles are compromised due to injury or surgical interventions. The potential impact of this work may extend to clinical practice, offering advancements in medical diagnosis and treatment strategies.
Erkki Somersalo (Committee Chair)
Daniela Calvetti (Committee Member)
Jenny Brynjarsdottir (Committee Member)
Kathryn Daltorio (Committee Member)
204 p.
Applied Mathematics; Biomechanics; Biomedical Engineering; Biomedical Research; Mathematics; Sports Medicine; Statistics
Inverse kinematics, Uncontrolled manifold, Markov Chain Monte Carlo, Feynman-Kac formula, Muscle Recruitment, Hierarchical Bayesian models and sparsity

Recommended Citations

Citations

  • Amankwah, M. G. (2024). Bayesian Analysis of Muscle Recruitment Patterns in Locomotion [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1711387309757226

    APA Style (7th edition)

  • Amankwah, Mercy. Bayesian Analysis of Muscle Recruitment Patterns in Locomotion. 2024. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1711387309757226.

    MLA Style (8th edition)

  • Amankwah, Mercy. "Bayesian Analysis of Muscle Recruitment Patterns in Locomotion." Doctoral dissertation, Case Western Reserve University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=case1711387309757226

    Chicago Manual of Style (17th edition)

case1711387309757226
© 2024, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.