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Title: Koopman Modeling of Human Gait Dynamics for Global Modal Analysis Using Periodic Motion Regularization
This paper presents a data driven global linear model of steady state walking dynamics. Instantaneous whole body angular momentum is a physics informed aggregate quantity used as a marker for dynamic balance during locomotion. Gait dynamics are often modeled as hybrid and nonlinear. We propose using Koopman Operators to model the gait stability dynamics with a global, linear model. This is achieved by augmenting the whole body angular momentum state variables with learned observables, or basis functions, such that the dynamics look linear in the lifted dimension. Considering that the gait dynamics are periodic, a regularization term that encourages the state transition matrix to be orthonormal is added to the loss term when learning the observables. This forces a periodic model to be learned and prevents the likelihood of unstable poles. A low average MSE was obtained over 2 gait cycles for different population types, each with slightly differing gait dynamics. Furthermore, this linear representation enables the use of linear analysis tools that could have clinical implications for assessing the gait of different patient groups.  more » « less
Award ID(s):
2133072
PAR ID:
10613388
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
American Automatic Control Council
Date Published:
Subject(s) / Keyword(s):
Koopman operators Gait dynamics Whole-body angular momentum
Format(s):
Medium: X
Location:
American Control Conference, Denver CO
Sponsoring Org:
National Science Foundation
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