Gaussian Process Trajectory Learning and Synthesis of Individualized Gait Motions
- Gaussian Process Trajectory Learning and Synthesis of Individualized Gait Motions
- 전창묵; 홍지수; 김승종; 박종우
- Gait rehabilitation; Robot rehabilitation; Gaussian process dynamical model; Gaussian process regression
- Issue Date
- IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
- VOL 27, NO 6-1245
- 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.
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