Walking Classification of Hip Joint Lower Limb Exoskeleton

Abstract

This paper presents a method which can classify the human walking gait cycle based on the Inertial Measurement Unit (IMU) and hip joint angle of lower limb exoskeleton information happened in one gait cycle. By using the information of IMU dan hip joint angle, each gait cycle on the leg supposed to be classified. In order to get the walking gait cycle prediction, the Neural Network (NN) has been chosen as the control system. The gait cycle prediction from NN, later on, will be classified using a simple comparison programming. This method tested on the prototype of the exoskeleton in real-time application. To verify the proposed method on the real-time application, some experiments have been carried out at different walking speed. The experimental results proved that the proposed method can classify the human walking gait cycle properly.