Gait Recognition and Robust Autonomous Location Method of Exoskeleton Robot Based on Machine Learning

Focusing on the autonomous location method of lower extremity exoskeleton robot in complex environment, this paper presents a gait type recognition method based on support vector machine (SVM)C and an autonomous location method based on inertial information mapping model and system reconstruction. In this paper, support vector machine is used to effectively recognize various kinds of conventional gait types of exoskeleton robots. The inertial information mapping models among different parts of the lower limbs of the robots under different gaits are established respectively. Aiming at the problem of the failure of the inertial measurement unit at the end of the limb in the impact or high overload motion, a robust autonomous location method based on system reconstruction is studied. The experiment results show that, using different neural network model parameters under different gaits can be used to reduce the complexity of the network model. While the inertial measurement unit at the end of the limb fails, the location performance of the exoskeleton robot navigation system based on this method, is equal to that of the inertial navigation system at the end of the limb with same sensor precision.