Our research focuses on exoskeleton robots that are wearable-mobile suits used to either restore the limb functionality or to increase the limb-strength for users such as amputees. We work on the identification of a set of relevant EMG features building a statistical pattern recognition framework for predicting the movement gestures from the electromyography signals using state-of-art techniques of digital signal processing and supervised machine learning. Such that an exoskeleton performance can be improved by providing accurate control commands from the biological electromyography (EMG) signals.

Background: Our research focuses on exoskeleton robots that are wearable-mobile suits used to either restore the limb functionality or to increase the limb-strength for users such as amputees. An exoskeleton performance can be improved by providing accurate control commands from the biological electromyography (EMG) signals.

We work on the identification of a set of relevant signal processing features, building a statistical pattern recognition framework for predicting the movement gestures from the electromyography signals using state-of-art techniques of supervised machine learning / Deep learning.