Personalizing the control law of an upper-limb exoskeleton using EMG signal

Abstract

Implementing an intuitive control law for an upperlimb exoskeleton dedicated to force augmentation is a challenging issue in the field of human-robot collaboration. The goal of this study is to adapt an EMG-based control system to a user based on individual characteristics. To this aim, a method has been designed to tune the parameters of control using objective criteria, improving user's feedback. The user's response time is used as an objective value to adapt the gain of the controller. The proposed approach was tested on 10 participants during a lifting task. Two different conditions have been used to control the exoskeleton: with a generic gain and with a personalized gain. EMG signals was captured on five muscles to evaluate the efficiency of the conditions and the user's adaptation. Results showed a statistically significant reduction of mean muscle activity of the deltoid between the beginning and the end of each situation (28.6%, standard deviation (SD) 13.5% to 17.2%, SD 7.3%, of Relative Maximal Contraction for the generic gain and from 24.9%, SD 8.5%, to 18%, SD 6.8%, of Relative Maximal Contraction for the personalized gain). When focusing on the first assisted movements, the personalized gain induced a mean activity of the deltoid significantly lower (29%, SD 8.0%, of Relative Maximal Contraction and 37.4%, SD 9.5%, of Relative Maximal Contraction, respectively). Subjective evaluation showed that the system with a personalized gain was perceived as more intuitive, and required less concentration when compared to the system with a generic gain.