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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| Hauptverfasser: | , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2022
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| In: |
IEEE Robotics and automation letters
Year: 2022, Jahrgang: 7, Heft: 2, Pages: 1566-1573 |
| ISSN: | 2377-3766 |
| DOI: | 10.1109/LRA.2021.3140055 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1109/LRA.2021.3140055
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| Verfasserangaben: | 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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| 245 | 1 | 0 | |a EMG-driven machine learning control of a soft glove for grasping assistance and rehabilitation |c 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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| 520 | |a 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. | ||
| 650 | 4 | |a agency | |
| 650 | 4 | |a assistive technology | |
| 650 | 4 | |a design | |
| 650 | 4 | |a electromyography | |
| 650 | 4 | |a exoskeleton | |
| 650 | 4 | |a hand | |
| 650 | 4 | |a machine learning algorithms | |
| 650 | 4 | |a Soft robotics | |
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| 700 | 1 | |a Alicea, Ryan |e VerfasserIn |4 aut | |
| 700 | 1 | |a Zhang, Xiaohui |d 1993- |e VerfasserIn |0 (DE-588)1256063568 |0 (DE-627)1800136102 |4 aut | |
| 700 | 1 | |a Xiloyannis, Michele |e VerfasserIn |4 aut | |
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| 700 | 1 | |a Papp, Emese |e VerfasserIn |4 aut | |
| 700 | 1 | |a Krzywinski, Jens |e VerfasserIn |4 aut | |
| 700 | 1 | |a Castellini, Claudio |e VerfasserIn |4 aut | |
| 700 | 1 | |a Masia, Lorenzo |e VerfasserIn |0 (DE-588)1186660864 |0 (DE-627)1665956569 |4 aut | |
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