A Reservoir Computing-based Controller for Intention Decoding of Upper-Limb Rehabilitative Exoskeletons

This work introduces a controller for an upperlimb rehabilitative exoskeleton based on reservoir computing (RC). The controller decodes the motor intention of the user by observing the electromyographic (EMG) activity of four upperlimb muscles and end effector (EE) kinematics and then assists the movements of upper-limb during the execution of planar reaching tasks. After tuning the hyperparameters of the RC, the controller was tested by three healthy participants wearing a shoulder-elbow active exoskeleton. The controller predicted the direction of reaching movements across eight possible targets positioned on a 25 cm circumference, achieving an average accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{7 4. 1 2 \%}$</tex>. Given the geometric structure of the task, we introduced a macro-direction measure of goodness (MDG) metric that considered both correct predictions and those corresponding to targets adjacent to the true one, resulting in an average performance of 96.63 %. Moreover, RC-ID outperformed a kinematics-only benchmark before kinematic onset and surpassed an EMG-only benchmark during the later phases of the reaching movement execution. Finally, effects of assistance were assessed by evaluating the variation of muscular activation during exoskeleton-assisted movements, which led to reductions up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$-47.4 \%$</tex> with respect the activations during unassisted movements.