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Fast and physiologically realistic predictive simulations of healthy and pathological human movement De Groote, Friedl
Description
Predictive simulations hold the potential to greatly expedite advances in understanding healthy and pathological movement. We developed a computationally efficient framework to predict human movement based on optimization of a performance criterion. The framework generates three-dimensional muscle-driven simulations, without relying on experimental data, in about 36 minutes on a standard laptopâ more than 20 times faster than existing simulationsâ by using direct collocation, implicit differential equations, and algorithmic differentiation. The simulations produce physiologically realistic gaits with varied gait speed, and changes in gait caused by muscle strength deficits or prosthesis use. We extended this framework to generate simulations that are robust to uncertainty (e.g., sensorimotor noise) to further increase the realism of our simulations. We expect these predictions to enable optimal design of treatments aiming to restore gait function.
Item Metadata
Title |
Fast and physiologically realistic predictive simulations of healthy and pathological human movement
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Creator | |
Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
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Date Issued |
2019-05-20T16:16
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Description |
Predictive simulations hold the potential to greatly expedite advances in understanding healthy and pathological movement. We developed a computationally efficient framework to predict human movement based on optimization of a performance criterion. The framework generates three-dimensional muscle-driven simulations, without relying on experimental data, in about 36 minutes on a standard laptopâ more than 20 times faster than existing simulationsâ by using direct collocation, implicit differential equations, and algorithmic differentiation. The simulations produce physiologically realistic gaits with varied gait speed, and changes in gait caused by muscle strength deficits or prosthesis use. We extended this framework to generate simulations that are robust to uncertainty (e.g., sensorimotor noise) to further increase the realism of our simulations. We expect these predictions to enable optimal design of treatments aiming to restore gait function.
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Extent |
50.0 minutes
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Subject | |
Type | |
File Format |
video/mp4
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Language |
eng
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Notes |
Author affiliation: KU Leuven
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Series | |
Date Available |
2020-09-14
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0394350
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URI | |
Affiliation | |
Peer Review Status |
Unreviewed
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Scholarly Level |
Researcher
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International