Neural Networks to Control Voluntary Movements in a Lower Limb Exoskeleton Using Electromyographic Signals

Abstract

A critical point in the human–exoskeleton interfaces is the multivariable voluntary control of several joints independently. The lower limb exoskeleton ESROB, which helpes a patient to perform the sit-to-stand postural exercise, has been used for testing a new control based on electromyographic (EMG) signals and artifical neural networks (ANN). The approach is of “admittance control”, i.e. the joints of the exoskeleton are controlled in speed, instead of torque as usual, by mixing an automatic postural control loop (especially for the balance) with a voluntary action of the patient through EMG signals, measured on suitable muscles of the legs and of the trunk, processed by ANN. Mixing the automatic postural loop with the voluntary action by the patient helps during the training of ANN to exercise the different degrees of freedom of the exoskeleton and during the control to improve balance. This chapter describes the automatic postual control of ESROB as well as the experiments of training and of multivariable voluntary control by the patient. In particular, exploiting the separation offered by the algorithms, it is shown that the three degrees of freedom of the exoskeleton are controlled independently intermixing, the automatic control loop, through external sensors, and the voluntary control of the patient.