Gaussian Process Learning and Interpolation of Gait Motion for Rehabilitation Robots

[ Information ]

2015 6th International Conference on Automation, Robotics and Applications (ICARA). IEEE, 2015, pp. 198-203.

[ Authors ]

Changmook Chun, Seung-Jong Kim, Jisoo Hong, and Frank C. Park

[ Abstract ]

We present an alternative approach to generate gait motion at arbitrary speed for gait rehabilitation robots. The methodology utilizes Gaussian process dynamical model (GPDM), which is a nonlinear dimensionality reduction technique. GPDM consists of a dynamics in low-dimensional latent space and a mapping from the space to configuration space, and GPDM learning results in the low-dimensional representation of training data and parameters for the dynamics and mapping. We use second-order Markov process dynamics model, and hence given a pair of initial points, the dynamics generates a latent trajectory at arbitrary speed. We use linear regression to obtain the initial points. Mapping from the latent to configuration spaces constructs trajectories of walking motion. We verify the algorithm with motion capture data from 50 healthy subjects, who walked on a treadmill at 1, 2, and 3 km/h. We show examples and compare the original and interpolated trajectories to prove the efficacy of the algorithm.