EMG-Based Control in a Test Platform for Exoskeleton with One Degree of Freedom

There are several efforts to use the electrical signals generated by the human muscles to control virtual or even physical devices. It is expected that, the development of this method will provide a natural way to control these devices, requiring little user training, depending on the task complexity. With respect to the control of exoskeletons from the electric signals generated by the muscles, it is desirable that the exoskeleton acts in synergy with the user using surface electromyography (sEMG) signals to detect user intentions. One of the challenges of this approach is the variability of the sEMG signals due to factors such as electrode positioning and conditions of the volunteer at the time of acquisition. In previous work, a procedure based on an Autoregressive with Exogenous Input (ARX) linear model was developed to translate sEMG from biceps, triceps and brachioradialis muscles to elbow joint angle. In this work, we developed a method based on a Genetic Algorithm (GA) to update the ARX model coefficients online to minimize the training periods and we have used the EMG signals to control a one-degree of freedom exoskeleton.vThe GA was able to obtain ARX model coefficients that generate the joint angle reference from the EMG signals. In addition, the joint angle references generated from the offline sEMG from three muscles via an ARX model were used to control a device. At this point we are carrying out tests with the exoskeleton using real-time signals from sEMG.