An emg-based robotic hand exoskeleton for bilateral training of grasp

This work presents the development and the preliminary experimental assessment of a novel EMG-driven robotic hand exoskeleton for bilateral active training of grasp motion in stroke. The system allows to control the grasping force required to lift a real object with an impaired hand, through the active guidance provided by a hand active exoskeleton, whose force is modulated by the EMG readings acquired on the opposite unimpaired arm. To estimate the grasping force, the system makes use of surface EMG recordings during grasping, developed on the opposite unimpaired arm, and of a neural network to classify the information. The design, integration and experimental characterization of the system during the grasp of two cylindrical objects is presented. The experimental results show that an optimal force tracking of the interaction force with the object can be achieved.