Gaussian Process Trajectory Learning and Synthesis of Individualized Gait Motions

Title
Gaussian Process Trajectory Learning and Synthesis of Individualized Gait Motions
Authors
전창묵홍지수김승종박종우
Keywords
Gait rehabilitation; Robot rehabilitation; Gaussian process dynamical model; Gaussian process regression
Issue Date
2019-06
Publisher
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Citation
VOL 27, NO 6-1245
Abstract
This paper proposes a Gaussian processbased method for trajectory learning and generation of individualized gait motions at arbitrary user-designated walkingspeeds, intended to be used in generating reference motions for robotic gait rehabilitation systems. We utilize a nonlinear dimension reduction technique based on Gaussian process dynamical models (GPDMs), in which the internal dynamics is modeled as a second-order Markov process evolving in a lower-dimensional latent space. After the GPDM parameters are identifi ed with training data obtained from gait motions of healthy subjects walking at different speeds, our method then employs Gaussian process regression (GPR) to predict the initial two states of the latent space dynamics from any arbitrary desired walking speed and the anthropometric parameters of the test subject. Motions are then generated by directly mapping the latent space dynamics to joint trajectories. Experimental studies involving more than 100 subjects indicate that our method generates gait patterns with 30% less mean square prediction errors compared to recent state-of-the-art methods, while also allowing for arbitrary user-specifi ed walking speeds.
URI
http://pubs.kist.re.kr/handle/201004/69491
ISSN
1534-4320
Appears in Collections:
KIST Publication > Article
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