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EMG-driven machine learning control of a soft glove for grasping assistance and rehabilitation

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,...

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Main Authors: Sierotowicz, Marek (Author) , Lotti, Nicola (Author) , Nell, Laura (Author) , Missiroli, Francesco (Author) , Alicea, Ryan (Author) , Zhang, Xiaohui (Author) , Xiloyannis, Michele (Author) , Rupp, Rüdiger (Author) , Papp, Emese (Author) , Krzywinski, Jens (Author) , Castellini, Claudio (Author) , Masia, Lorenzo (Author)
Format: Article (Journal)
Language:English
Published: 2022
In: IEEE Robotics and automation letters
Year: 2022, Volume: 7, Issue: 2, Pages: 1566-1573
ISSN:2377-3766
DOI:10.1109/LRA.2021.3140055
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1109/LRA.2021.3140055
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Author Notes:Marek Sierotowicz, Nicola Lotti, Laura Nell, Francesco Missiroli, Ryan Alicea, Xiaohui Zhang, Michele Xiloyannis, Rüdiger Rupp, Emese Papp, Jens Krzywinski, Claudio Castellini, and Lorenzo Masia
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Summary: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.
Item Description:Gesehen am 15.02.2022
Physical Description:Online Resource
ISSN:2377-3766
DOI:10.1109/LRA.2021.3140055

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