Simulating Motion Success With Muscle Deficiency in a Musculoskeletal Model Using Reinforcement Learning

Author

Daniel Castillo, The University of Texas Rio Grande Valley

Date of Award

8-2023

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Dong-Chul Kim

Second Advisor

Erik Enriquez

Third Advisor

Emmett Tomai

Abstract

Humans possess an extraordinary ability to execute complex movements, captivating the attention of researchers who strive to develop methods for simulating these actions within a physics-based environment. Motion Capture data stands out as a crucial tool among the proven approaches to tackle this challenge. In this research, we explore the effects of decreased muscle force on the body's capacity to perform various tasks, ranging from simple walking to executing complex jumping jacks. Through a systematic reduction of the allowed force applied to individual muscles or muscle groups, we aim to identify the threshold at which the body's muscles tolerate the deficiency before the motion becomes unattainable. Additionally, we seek to analyze how the model adapts its muscle activation levels, in scenarios where the motion is still achievable despite the applied deficiencies.

Comments

Copyright 2023 Daniel Castillo. All Rights Reserved.

https://go.openathens.net/redirector/utrgv.edu?url=https://www.proquest.com/dissertations-theses/simulating-motion-success-with-muscle-deficiency/docview/2862069195/se-2?accountid=7119

Recommended Citation

Castillo, Daniel, "Simulating Motion Success With Muscle Deficiency in a Musculoskeletal Model Using Reinforcement Learning" (2023). Theses and Dissertations. 1325.
https://scholarworks.utrgv.edu/etd/1325