Sensitivity Analysis of a Parametric Optimization Model Used to Identify Neural and Non-Neural Contributors to Joint Stiffness
R.M. Bouman (TU Delft - Mechanical Engineering)
W. Mugge – Mentor (TU Delft - Biomechatronics & Human-Machine Control)
Alfred C. Schouten – Mentor (TU Delft - Biomechanical Engineering)
Jurriaan H. de Groot – Mentor
Frank J H Gijsen – Graduation committee member (Erasmus MC)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
The influence of intrinsic properties (mass, stiffness, and alignment) on ankle joint dynamics, along with the sensitivity of a parametric optimization model to changes in these properties, was investigated. Experiments were performed on two young adults using a robotic manipulator to apply controlled perturbations comprising multiple ramp-and-holds while recording position, torque, and electromyography (EMG) data.
Position, torque, and EMG data were subsequently used as input for the computational model, producing a modeled torque. The model, designed to parametrize neural and non-neural contributors to joint stiffness associated with spasticity, employs a 23-parameter Hill-type neuromuscular framework to estimate muscle properties by minimizing the difference between modeled and measured torque.
Experimental results revealed that mass and stiffness significantly influenced the required torque, particularly during the hold phases, while alignment had minimal effects. Model sensitivity analysis showed similar trends, with stiffness having the greatest impact on torque, followed by mass. Changes in alignment were minimal, highlighting its redundancy.
Discrepancies were observed between model predictions and experimental data in capturing the effects of mass and stiffness changes. The model predicted equivalent mass effects regardless of angle, whereas experimental data exhibited angle dependency. Furthermore, modifying stiffness did not affect the dynamic phases of the modeled torque but did influence the dynamic phases of the measured torque.
These findings highlight the complex interplay between biomechanical factors and torque generation. Future work should refine the model to capture physical nuances better, improve experimental setups to control joint angles and optimize model parameters in a staged approach. Such efforts will advance the development of the model and deepen the understanding of spasticity, paving the way for more accurate and personalized treatment strategies.