EMG-Driven Machine Learning Control of a Soft Glove for Grasping Assistance and Rehabilitation
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Date
2022-04
Publication Type
Journal Article
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yes
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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 detectthe 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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published
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Journal / series
Volume
7 (2)
Pages / Article No.
1566 - 1573
Publisher
IEEE
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Software
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Organisational unit
03654 - Riener, Robert / Riener, Robert