EMG-driven machine learning control of a soft glove for grasping assistance and rehabilitation - Sierotowicz, Marek; Lotti, Nicola; Nell, Laura; Missiroli, Francesco; Alicea, Ryan; Zhang, Xiaohui; Xiloyannis, Michele; Rupp, Rüdiger; Papp, Emese; Krzywinski, Jens; Castellini, Claudio; Masia, Lorenzo (IEEE Robotics and automation letters, 2022)
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 detect the 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 he implemented by means of a machine learning algorithm.
Gesehen am 15.02.2022