Gait event-based human-robot synchrony for gait rehabilitation using adaptive oscillator

Abstract

In the field of gait training with rehabilitation robots, reference robot trajectories that are synchronous to human gait is inevitable. In this paper, a novel adaptive oscillator-based synchronization algorithm using gait events during overground walking is proposed. The gait events are detected with hidden Markov model (HMM), the feature vector of which is measured with wearable inertia measurement unit (IMU) sensors. An adaptive oscillator, which was proposed to explain the mechanism of the synchronous flashing of fireflies, is implemented to estimate the phase of the human gait using the gait events (pulse) information. The reference trajectory is generated with a look-up table according to the estimated phase. The proposed algorithm has been implemented in a portable knee-ankle-robot and tested on a healthy subject. Experimental results demonstrate that the proposed algorithm is effective, flexible and efficient to achieve human-robot synchrony.