TY - GEN
T1 - Gaussian process learning and interpolation of gait motion for rehabilitation robots
AU - Chun, Changmook
AU - Kim, Seung Jong
AU - Hong, Jisoo
AU - Park, Frank C.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/4/6
Y1 - 2015/4/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84928782193&partnerID=8YFLogxK
U2 - 10.1109/ICARA.2015.7081147
DO - 10.1109/ICARA.2015.7081147
M3 - Conference contribution
AN - SCOPUS:84928782193
T3 - ICARA 2015 - Proceedings of the 2015 6th International Conference on Automation, Robotics and Applications
SP - 198
EP - 203
BT - ICARA 2015 - Proceedings of the 2015 6th International Conference on Automation, Robotics and Applications
A2 - Bailey, Donald
A2 - Gupta, G. Sen
A2 - Demidenko, Serge
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Automation, Robotics and Applications, ICARA 2015
Y2 - 17 February 2015 through 19 February 2015
ER -