EMG-Driven Machine Learning Control of a Soft Glove for Grasping Assistance and Rehabilitation
Marek Sierotowicz, Nicola Lotti, Laura Nell, Francesco Missiroli, Ryan Alicea, Xiaohui Zhang, Michele Xiloyannis, Rüdiger Rupp, Emese Papp, Jens Krzywinski, Claudio Castellini, Lorenzo Masia
Abstract
In the field of rehabilitation robotics, transparent, precise and intuitive control of hand exoskeletons still represents a substantial challenge. In particular, the use of compliant systems often leads to a trade-off between lightness and material flexibility, and control precision. In this letter, we present a compliant, actuated glove with a control scheme to detectthe user’s motion intent, which is estimated by a machine learning algorithm based on muscle activity. Six healthy study participants used the glove in three assistance conditions during a force reaching task. The results suggest that active assistance from the glove can aid the user, reducing the muscular activity needed to attain a medium-high grasp force, and that closed-loop control of a compliant assistive glove can successfully be implemented by means of a machine learning algorithm.