Abstract
Bionic reconstruction consists in the replacement of lost limbs with mechatronic devices. In upper limb substitution, a man-machine interface that establishes a link between the user’s nervous system and the robotic limb (prosthesis) is needed. This interfacing is commonly done with the remnant muscles above the amputation, either through their physiological innervation or using the surgical approach of targeted muscle reinnervation. Muscle interfacing or myoelectric control consists in the recording of electromyographic (EMG) signals for extracting control signals to command prostheses. In commercial systems, the intensity of muscle activity is extracted from the EMG and used for single degree of freedom activation (direct control). Over the past decades, the academic research has progressed to more sophisticated approaches but, surprisingly, none of these academic achievements has been implemented in commercial systems so far. The academic state-of-the-art relies on pattern recognition as a method to control multiple motor tasks using a relatively small number of electrodes. However, this approach has important limitations. We proposed a change of focus on myocontrol research in the direction of approaches for simultaneous and proportional control of multiple degrees of freedom, which are based on regression methods. Moreover, the exclusive use of EMG as a source for feed-forward control of prostheses may not be sufficient. Therefore, methods that integrate the EMG information with that from other sensors, within semiautonomous systems, are described. The talk will cover these topics with a discussion on the major challenges in filling the gap between commercial/clinical and academic methods for myocontrol. Finally, the new approach of bionic reconstruction of upper limb function following elective amputation will be introduced.