Learning a Predictive Model of Human Gait for the Control of a Lower-limb Exoskeleton

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.