Abstract

The complexity of the human musculoskeletal system presents challenges in accurately identifying its characteristics, particularly due to the presence of redundant actuators on a single joint. Non-invasive measures are necessary to overcome these challenges. Optimization algorithms have emerged as a crucial tool to advance subject-specific musculoskeletal modeling allows a more realistic representation of biomechanical behaviors, enhancing our understanding of human movement and enabling better clinical decision-making. Furthermore, optimization algorithms play a vital role in customizing rehabilitation and assistive devices, such as orthoses and prostheses. The current ankle-foot orthosis (AFO) stiffness measurement methods require bulky, complex designs, and often permanent modification of the AFO. To address this, we proposed the Ankle Assistive Device Stiffness (AADS) test method, which utilizes a simple design jig and motion capture system. In our method we employed a static optimization algorithm to estimate external forces and AFO torque, providing reliable stiffness quantification. The AADS test demonstrated high precision among different operators and trials, with an overall percent error within ±6%. In the pursuit of accurately measuring muscle-tendon parameters, various techniques, including shear waves, have been utilized. However, these techniques often are invasive or lack the ability to provide quantitative measurements. In our second study, we introduced a noninvasive method for estimating passive muscle-tendon parameters (PMPs) in knee flexors/extensors and the Achilles tendon. We employed a direct collocated optimal control algorithm and evaluated the precision of the proposed method through simulation, replica leg experiments, and in-vivo experiments involving 10 subjects. The estimated range for tendon slack length was reported between 0.59 and 1.13, while the median of tendon stiffness was 421 KN/m. Muscle stiffness ranged between 473 N/m and 1200 N/m. The average root mean square error (RMSE) between experimentally collected joint kinematics and kinetics and forward dynamic verification was less than 0.56° and 12 mN.m/Kg, respectively.

Notes

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Graduation Date

2023

Semester

Summer

Advisor

Choi, Hwan

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Mechanical and Aerospace Engineering

Degree Program

Mechanical Engineering

Identifier

CFE0009896; DP0028429

URL

https://purls.library.ucf.edu/go/DP0028429

Language

English

Release Date

February 2024

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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