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Simulated Work Loops Predict Human C...
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Link, Jenna Elizabeth,
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Simulated Work Loops Predict Human Cycling: Modeling Maximal Fatigue and Sub-Maximal Intensities /
Record Type:
Electronic resources : Monograph/item
Title/Author:
Simulated Work Loops Predict Human Cycling: Modeling Maximal Fatigue and Sub-Maximal Intensities // Jenna Elizabeth Link.
Author:
Link, Jenna Elizabeth,
Description:
1 electronic resource (131 pages)
Notes:
Source: Dissertations Abstracts International, Volume: 86-07, Section: B.
Contained By:
Dissertations Abstracts International86-07B.
Subject:
Biomechanics. -
Online resource:
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31481829
ISBN:
9798302159915
Simulated Work Loops Predict Human Cycling: Modeling Maximal Fatigue and Sub-Maximal Intensities /
Link, Jenna Elizabeth,
Simulated Work Loops Predict Human Cycling: Modeling Maximal Fatigue and Sub-Maximal Intensities /
Jenna Elizabeth Link. - 1 electronic resource (131 pages)
Source: Dissertations Abstracts International, Volume: 86-07, Section: B.
Neuromuscular modeling enables researchers to investigate how the neuromuscular system interacts with its environment to generate movement. Recently, it has been shown that with maximal excitation, humans optimize muscle onset and offset timing to maximize power production during cycling. This simulated work loop technique could allow researchers to understand the functional capacity of muscle given known contractile characteristics and muscle excitation. This dissertation aimed to expand upon the simulated work loop technique to understand muscle performance capacity following fatigue and during sub-maximal activity. Three specific aims were carried out to achieve this objective. First, an experimental study using a custom bi-kinetic ergometer was conducted to determine if cadence influenced the development of fatigue and should, therefore, be included in a fatigable muscle model. Measured changes in the fatigued torque-cadence relationship were compared following 25s of maximal intensity cycling at three cadences. No significant difference in fatigue was found across cadence (p = 0.371). Second, a fatigable muscle model was developed to simulate muscle power as a function of changes in muscle contractile characteristics with fatigue. Since several muscle contractile characteristics are known to change with fatigue, a systematic review was conducted to identify relationships between changes in force-velocity and activation/deactivation dynamics with fatigue for use in developing the fatigable muscle model. The simulated work loop technique was then conducted using the fatigable muscle model. The resulting simulated fatigued leg power closely replicated experimentally derived kinetics following fatigue (r2 = 0.899, ICC = 0.899). Third, the simulated work-loop technique was used to predict power production during sub-maximal cycling at progressively reduced muscle excitation levels while optimizing muscle onset/offset timing to maximize power production. The resulting simulated leg power closely replicated experimentally derived kinetics at five sub-maximal intensities (850W: r2 = 0.872, ICC = 0.875; 700W: r2 = 0.854, ICC = 0.889; 550W: r2 = 0.802, ICC = 0.857; 400W: r2 = 0.763, ICC = 0.874; 250W: r2 = 0.646, ICC = 0.727). In conclusion, this dissertation has shown that the simulated work loop technique can be expanded to study neuromuscular function following fatigue or at sub-maximal intensities.
English
ISBN: 9798302159915Subjects--Topical Terms:
548685
Biomechanics.
Subjects--Index Terms:
Cycling
Simulated Work Loops Predict Human Cycling: Modeling Maximal Fatigue and Sub-Maximal Intensities /
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Neuromuscular modeling enables researchers to investigate how the neuromuscular system interacts with its environment to generate movement. Recently, it has been shown that with maximal excitation, humans optimize muscle onset and offset timing to maximize power production during cycling. This simulated work loop technique could allow researchers to understand the functional capacity of muscle given known contractile characteristics and muscle excitation. This dissertation aimed to expand upon the simulated work loop technique to understand muscle performance capacity following fatigue and during sub-maximal activity. Three specific aims were carried out to achieve this objective. First, an experimental study using a custom bi-kinetic ergometer was conducted to determine if cadence influenced the development of fatigue and should, therefore, be included in a fatigable muscle model. Measured changes in the fatigued torque-cadence relationship were compared following 25s of maximal intensity cycling at three cadences. No significant difference in fatigue was found across cadence (p = 0.371). Second, a fatigable muscle model was developed to simulate muscle power as a function of changes in muscle contractile characteristics with fatigue. Since several muscle contractile characteristics are known to change with fatigue, a systematic review was conducted to identify relationships between changes in force-velocity and activation/deactivation dynamics with fatigue for use in developing the fatigable muscle model. The simulated work loop technique was then conducted using the fatigable muscle model. The resulting simulated fatigued leg power closely replicated experimentally derived kinetics following fatigue (r2 = 0.899, ICC = 0.899). Third, the simulated work-loop technique was used to predict power production during sub-maximal cycling at progressively reduced muscle excitation levels while optimizing muscle onset/offset timing to maximize power production. The resulting simulated leg power closely replicated experimentally derived kinetics at five sub-maximal intensities (850W: r2 = 0.872, ICC = 0.875; 700W: r2 = 0.854, ICC = 0.889; 550W: r2 = 0.802, ICC = 0.857; 400W: r2 = 0.763, ICC = 0.874; 250W: r2 = 0.646, ICC = 0.727). In conclusion, this dissertation has shown that the simulated work loop technique can be expanded to study neuromuscular function following fatigue or at sub-maximal intensities.
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https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31481829
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