SIMULATING WALKING FOR DEVELOPING METABOLIC ESTIMATION METHODS
Author ORCID Identifier
https://orcid.org/0000-0003-1999-0246
Advisor Information
Philippe Malcolm
Presentation Type
Poster
Start Date
26-3-2021 12:00 AM
End Date
29-3-2021 12:00 AM
Abstract
Breadth-by-breadth oxygen consumption is a proxy for muscle energy expenditure referred to as metabolic rate. The measurement of oxygen consumption is useful for a variety of sciences but serves as an important indicator of performance for assistive devices during walking where, more specifically, reductions in metabolic rate in this abstract are considered a positive outcome. Correct estimation of metabolic rate hinges upon exercise of sufficient duration. However, various clinical populations cannot walk for this duration, meaning that testing and improvement of assistive devices for these people lose the critical reference of success, that is, metabolic rate.
Our lab has previously reported reductions in metabolic rate during walking using various assistive forces at the body’s center of mass. Utilizing these metabolic data from this experiment, we plan to improve methods of metabolic rate using predictive simulation methods. We have utilized a neuromuscular model (modified to be similar to our experiment) by Song & Geyer (2015) to generate muscle data that has been shown to be similar to humans. We have applied this muscle data to a metabolic rate estimation method developed by Umberger et al. (2003). Serving as preliminary data, these methods of simulation have shown relatively similar results for nine out of twelve successfully optimized assistive force conditions. The best preliminary simulation result matches experimental reductions at -25%. Following improvements to our model’s optimization process, we aim to generate otherwise unattainable data for improving metabolic rate estimation methods and assistive device designs for patient populations.
Scheduling Link
1
SIMULATING WALKING FOR DEVELOPING METABOLIC ESTIMATION METHODS
Breadth-by-breadth oxygen consumption is a proxy for muscle energy expenditure referred to as metabolic rate. The measurement of oxygen consumption is useful for a variety of sciences but serves as an important indicator of performance for assistive devices during walking where, more specifically, reductions in metabolic rate in this abstract are considered a positive outcome. Correct estimation of metabolic rate hinges upon exercise of sufficient duration. However, various clinical populations cannot walk for this duration, meaning that testing and improvement of assistive devices for these people lose the critical reference of success, that is, metabolic rate.
Our lab has previously reported reductions in metabolic rate during walking using various assistive forces at the body’s center of mass. Utilizing these metabolic data from this experiment, we plan to improve methods of metabolic rate using predictive simulation methods. We have utilized a neuromuscular model (modified to be similar to our experiment) by Song & Geyer (2015) to generate muscle data that has been shown to be similar to humans. We have applied this muscle data to a metabolic rate estimation method developed by Umberger et al. (2003). Serving as preliminary data, these methods of simulation have shown relatively similar results for nine out of twelve successfully optimized assistive force conditions. The best preliminary simulation result matches experimental reductions at -25%. Following improvements to our model’s optimization process, we aim to generate otherwise unattainable data for improving metabolic rate estimation methods and assistive device designs for patient populations.