Abstract:In the research of companion robots, gait phase detection is the key to maintaining manmachine synchronous motion. However, improving detection accuracy requires collecting and analyzing more gait phase information, which results in a long detection delay and is unable to meet the realtime requirements. In this paper, a progressive gait phase detection algorithm targeting to synchronous motion of companion robots is proposed. The algorithm mainly constructs the physical layer and decision layer of the probabilistic generative model based on the inertial measurement unit and Bayesian information criterion, and performs preliminary rapid gait phase detection; when the detection fails to reach the decision threshold, a memory network is introduced in the decision layer to predict the gait phase parameters for next period of time, thereby provide more decision information for the probabilistic generative model, and progressively complete the accurate incremental detection of the gait phase based on multiple decision results. The experiment results show that the proposed gait phase detection algorithm achieves an accuracy of 978%; the decision time is 283 ms, which is about 30% reduction compared with the adaptive Bayesian algorithm.