EMG-driven Machine Learning Control of a Soft Glove for Grasping Assistance and Rehabilitation.

Sierotowicz M, Lotti N, Nell L, Missiroli F, Alicea R, Zhang X, Xiloyannis M, Rupp R, Papp E, Krzywinski J, Masia L, Castellini C (2022)


Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2022

Journal

Book Volume: 7

Pages Range: 1566-1573

Journal Issue: 2

URI: https://ieeexplore.ieee.org/document/9669054

DOI: 10.1109/LRA.2021.3140055

Open Access Link: https://scholar.google.com/scholar?oi=bibs&cluster=13584365578456203163&btnI=1&hl=de

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 paper, 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 be implemented by means of a machine learning algorithm.

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How to cite

APA:

Sierotowicz, M., Lotti, N., Nell, L., Missiroli, F., Alicea, R., Zhang, X.,... Castellini, C. (2022). EMG-driven Machine Learning Control of a Soft Glove for Grasping Assistance and Rehabilitation. IEEE Robotics and Automation Letters, 7(2), 1566-1573. https://dx.doi.org/10.1109/LRA.2021.3140055

MLA:

Sierotowicz, Marek, et al. "EMG-driven Machine Learning Control of a Soft Glove for Grasping Assistance and Rehabilitation." IEEE Robotics and Automation Letters 7.2 (2022): 1566-1573.

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