Exoskeleton Online Learning and Estimation of Human Walking Intention Based on Dynamical Movement Primitives

Abstract

Human walking intention estimation is a critical step for the active assistance control of lower limb exoskeleton, because the purpose of active assistance control is human motion assistance rather than human motion tracking. Complying with human walking intention is the basic requirement of human walking assistance. Hence, the human walking intention must be estimated first to ensure the exoskeleton will not impede human motion. Actually, estimating human walking intention is to estimate human joint torque during walking. In order to estimate a smooth personalized human joint torque profile, an online learning and prediction algorithm of human joint trajectory and joint torque is proposed in this article. The algorithm is based on the dynamical movement primitives model which is used for online learning and predicting human joint trajectory which is substituted into the human dynamics model to estimate the human joint torque. The results of human walking experiments demonstrate that the proposed algorithm can not only predict a smooth human joint trajectory and joint torque profile in real time but also compensate the phase delay caused by sensor signal filtering. Hence, the proposed algorithm is suitable for the active walking assistance control of exoskeleton.