Use of the Human Walking Gait Cycle for Assistive Torque Generation for the Hip Joint Exoskeleton

The development of an assistive robot to assist human beings in walking normally is a difficult task. One of the main challenges lies in understanding the intention to walk, as an initial phase before walking commences. In this work, we classify the human gait cycle based on data from an inertial moment unit sensor and information on the angle of the hip joint and use the results as initial signals to produce a suitable assistive torque for a lower limb exoskeleton. A neural network module is used as a prediction module to identify the intention to walk based on the gait cycle. A decision tree method is implemented in our system to generate the assistive torque, and a prediction of the human gait cycle is used as a reference signal. Real-time experiments are carried out to verify the performance of the proposed method, which can differentiate between various types of walking. The results show that the proposed method is able to predict the intention to walk as an initial phase and is also able to provide an assistive torque based on the information predicted for this phase.