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| Verfasst von: | Sierotowicz, Marek [VerfasserIn]  |
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| | Lotti, Nicola [VerfasserIn] |
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| | Nell, Laura [VerfasserIn] |
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| | Missiroli, Francesco [VerfasserIn] |
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| | Alicea, Ryan [VerfasserIn] |
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| | Zhang, Xiaohui [VerfasserIn] |
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| | Xiloyannis, Michele [VerfasserIn] |
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| | Rupp, Rüdiger [VerfasserIn] |
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| | Papp, Emese [VerfasserIn] |
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| | Krzywinski, Jens [VerfasserIn] |
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| | Castellini, Claudio [VerfasserIn] |
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| | Masia, Lorenzo [VerfasserIn] |
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| Titel: | EMG-driven machine learning control of a soft glove for grasping assistance and rehabilitation |
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| Verf.angabe: | 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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| Jahr: | 2022 |
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| Umfang: | 8 S. |
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| Fussnoten: | Gesehen am 15.02.2022 |
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| Titel Quelle: | Enthalten in: IEEE Robotics and automation letters |
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| Ort Quelle: | New York, N.Y. : Institute of Electrical and Electronics Engineers, 2016 |
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| Jahr Quelle: | 2022 |
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| Band/Heft Quelle: | 7(2022), 2, Seite 1566-1573 |
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| ISSN Quelle: | 2377-3766 |
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| 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 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. |
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| DOI: | doi:10.1109/LRA.2021.3140055 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext: https://doi.org/10.1109/LRA.2021.3140055 |
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| | DOI: https://doi.org/10.1109/LRA.2021.3140055 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | agency |
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| | assistive technology |
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| | design |
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| | electromyography |
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| | exoskeleton |
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| | hand |
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| | machine learning algorithms |
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| | Soft robotics |
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| K10plus-PPN: | 1789620392 |
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| Verknüpfungen: | → Zeitschrift |
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EMG-driven machine learning control of a soft glove for grasping assistance and rehabilitation / Sierotowicz, Marek [VerfasserIn]; 2022 (Online-Ressource)
