Abstract

Tremor affects millions of people and many patients desire alternative treatment options to medication or neural surgery. Peripheral suppression techniques are gaining greater use, but are currently applied in a trial-and-error method. To optimize these techniques, the muscles most responsible for an individual patient's tremor need to be identified. In this dissertation, I explored two parallel paths that both could aid in identifying muscles responsible for tremor. The first method utilizies measured data and a technique (coherence) that quantifies the frequency dependent correlation between two signals. Using coherence to identify muscles contributing to tremor requires at least two parts: an analysis of how tremor content is shared between muscles, and an analyis between muscle activity and joint/hand motion. The interpretation of the second analysis depends on the results of the first. The second method of identifying responsible muscles uses a mathematical model of the upper limb. With a validated model established techniques can be used to quantify the contribution to the output from each input. However, the accuracy of the model that has been previously used in the Neuromechanics Research Group had not been quantified. To evaluate the accuracy of this model, I used measured muscle activity as the input to generate simulated tremor and compared that to the measured tremor. From the first method, I found that synergistic muscles tend to share tremor content and do so in phase with each other. Therefore, tremor is likely due to a group of muscles rather than a single muscle. Additionally, I observed that the elbow flexor and wrist extensor muscles tended to be most correlated with tremor and should therefore be considered in peripheral suppression techniques. The second method revealed that while this upper-limb model shows potential to predict cases of severe tremor, improved model parameters must be identified through measurement or estimation techniques before the model should be used as it currently over-predicts the tremor.

Degree

PhD

College and Department

Ira A. Fulton College of Engineering; Mechanical Engineering

Rights

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

Date Submitted

2024-04-08

Document Type

Dissertation

Handle

http://hdl.lib.byu.edu/1877/etd13136

Keywords

coherence, sEMG, musculoskeletal modeling, tremor

Language

english

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