Modelling of a biped kinematics, analysis and simulation of the human walk for a future lower-limb exoskeleton

Humanoid biped robots are typically complex in design, having numerous degrees-of-freedom (DOF) due to the ambitious goal of mimicking the human gait. This thesis describes walking control algorithm for the stable walking of a lower-limb exoskeletons of a fifteen DOF (six for each leg and three for the support point). First of all, the gait locomotion, the human foot and the zero moment point (ZMP) get studied from an anatomical and kinesiological point of view. Input variables are imported from the big dataset of the University of Dallas. These are used for the computation of ZMP and, according to the Linear Inverted Pendulum Model (LIPM), the COG pattern is generated. Consequently, an estimation algorithm is used to fit the calculated trajectories and the joint angles going from a dataset to a kinematic model. Considering the study of kinematic model, this work will be used for the interaction with the patient and it will take place by controlling the motors of the joints through electromyographic signals measured by the muscles of the legs and the torso. Moreover, it will be possible to create an exoskeleton adding an online analysis of the EMG signals through deep-learning, where the patient can close the control loop through his/her movements.