Abstract

Tremor is a common neurological condition that significantly impairs the quality of life for millions. Despite this prevalence, the underlying mechanisms of its propagation remain poorly understood, and current treatment options often rely on trial-and-error methods. Moreover, the most common type, Essential Tremor (ET), presents a unique challenge due to its heterogeneity--patients exhibit a wide range of symptoms, severities, and responses to treatment. This thesis primarily advances tremor understanding through computational modeling, with a secondary focus on data-driven subtyping of ET via cluster analysis. These parallel paths aim to improve both mechanistic insight and clinical classification. Tremor propagation refers to the process by which the upper limb mixes and spreads tremorogenic neural drive to produce joint and hand tremor. In contrast, tremor decomposition involves inferring the underlying muscle contributions from observed tremor patterns. Previous models have primarily addressed these processes using only the 15 major superficial muscles of the upper limb. In this work, I advanced these models in two key stages. First, I incorporated 35 additional upper-limb muscles and simulated three new postures to reevaluate principles of joint-level tremor. Second, I extended the model to hand-level tremor and integrated posture-specific peak muscle forces, enabling an evaluation of tremor where it matters most. These improvements facilitated a more detailed and comprehensive understanding of how simulated tremor propagates through the musculoskeletal system. Several key findings emerged. First, posture has a substantial influence on tremor characteristics, affecting the muscles involved, magnitude, and spatial distribution at both joint and hand levels. Second, although many upper-limb muscles are positioned to contribute to tremor, the 15 major superficial muscles account for the patterns of joint tremor and largely place among the top contributors to hand tremor. Third, steady-state, narrow-band tremorogenic activity causes the hand to trace an elliptical path, regardless of tremorgenic inputs. Fourth, the combination of individual muscle ellipses produces a relatively flat ellipsoid with a dominant plane nearly perpendicular to the hand's long axis. In the second research path, I explored using cluster analysis to subtype ET. This began with a review of the literature on A-Posteriori tremor clustering, which I supplemented with an in-depth review of the movement features, measurement devices, and recording methods used in such studies. I then extracted kinematic features from a real-world dataset and performed cluster analysis. My review revealed that, while kinematic features are well-established for distinguishing known tremor types (e.g., ET vs Parkinson's disease), their use in uncovering novel subtypes remains largely unexplored. Data analysis revealed that many simple kinematic features are highly correlated and primarily reflect tremor magnitude, underscoring the importance of feature selection to uncover groupings beyond severity. Despite these challenges, the analysis revealed multiple distinct clusters within ET, justifying future research in this area. Taken together, these approaches--computational modeling and data-driven subtyping--offer promising directions for advancing the understanding, diagnosis, and treatment of ET.

Degree

MS

College and Department

Ira A. Fulton College of Engineering; Mechanical Engineering

Rights

https://lib.byu.edu/about/copyright/

Date Submitted

2025-08-07

Document Type

Thesis

Keywords

Essential Tremor, ET, postural tremor, tremor propagation, biomechanical modeling, tremor decomposition, neuromusculoskeletal, upper limb, frequency response functions, hand, cluster analysis, disease subtyping, tremor axis, tremor classification, tremor ellipse, Monte Carlo

Language

english

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