EMG-Based Detection of User's Intentions for Human-Machine Shared Control of an Assistive Upper-Limb Exoskeleton

To assist people with disabilities, exoskeletons must be provided with human-machine interfaces (HMI) capable to identify the user's intentions and enable cooperative interaction. Electromyographic (EMG) signals could be suitable for this purpose, but their usability and effectiveness for shared control schemes in assistive devices is currently unclear. Here we developed advanced machine learning (ML) algorithms for detecting the user's motion intention and decoding the intended movement direction, and discuss their applicability to the control of an upper-limb exoskeleton used as an assistive device for people with severe arm disabilities.