DocumentCode :
259963
Title :
Learning a Predictive Model of Human Gait for the Control of a Lower-limb Exoskeleton
Author :
Aertbelien, Erwin ; De Schutter, Joris
Author_Institution :
Dept. of Mech. Eng., KU Leu-ven, Belgium
fYear :
2014
fDate :
12-15 Aug. 2014
Firstpage :
520
Lastpage :
525
Abstract :
For an intelligent dynamic motion interaction between a human and a lower-limb exoskeleton, it is necessary to predict the future evolution of the joint gait trajectories and to detect which phase of the gait pattern is currently active. A model of the gait trajectories and of the variations on these trajectories is learned from an example data set. A gait prediction module, based on a statistical latent variable model, is able to predict, in real-time, the future evolution of a joint trajectory, an estimate of the uncertainty on this prediction, the timing along the trajectory and the consistency of the measurements with the learned model. The proposed method is validated using a data set of 54 trials of children walking at three different velocities.
Keywords :
learning systems; motion control; predictive control; robots; trajectory control; gait pattern; gait prediction module; human gait predictive model; intelligent dynamic motion interaction; joint gait trajectory model; joint trajectory evolution; lower-limb exoskeleton control; statistical latent variable model; Exoskeletons; Hip; Joints; Knee; Predictive models; Trajectory; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Robotics and Biomechatronics (2014 5th IEEE RAS & EMBS International Conference on
Conference_Location :
Sao Paulo
ISSN :
2155-1774
Print_ISBN :
978-1-4799-3126-2
Type :
conf
DOI :
10.1109/BIOROB.2014.6913830
Filename :
6913830
Link To Document :
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