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sEMG-based Control Strategy for a Hand Exoskeleton System / by Nicola Secciani

By: Secciani, Nicola, autor
Material type: materialTypeLabelE-bookSeries: (Springer Theses Recognizing Outstanding Ph.D. Research, 2190-5061).Publisher: Cham : Springer International Publishing, 2022Edition: 1st edition 2022.Description: 1 recurso en línea (XVIII, 91 páginas) : 46 ilustraciones, 37 ilustraciones a color.ISBN: 9783030902834.Subject: Robótica médica | OrtopediaOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
Introduction -- Background: first-stage device -- The new control system -- Tests and results.
Summary: This book reports on the design and testing of an sEMG-based control strategy for a fully-wearable low-cost hand exoskeleton. It describes in detail the modifications carried out to the electronics of a previous prototype, covering in turn the implementation of an innovative sEMG classifier for predicting the wearer's motor intention and driving the exoskeleton accordingly. While similar classifier have been widely used for motor intention prediction, their application to wearable device control has been neglected so far. Thus, this book fills a gap in the literature providing readers with extensive information and a source of inspiration for the future design and control of medical and assistive devices.
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias de la Salud RD755 2022 EB (Browse shelf(Opens below)) Acceso electrónico eBook.09012731
Total holds: 0

Introduction -- Background: first-stage device -- The new control system -- Tests and results.

This book reports on the design and testing of an sEMG-based control strategy for a fully-wearable low-cost hand exoskeleton. It describes in detail the modifications carried out to the electronics of a previous prototype, covering in turn the implementation of an innovative sEMG classifier for predicting the wearer's motor intention and driving the exoskeleton accordingly. While similar classifier have been widely used for motor intention prediction, their application to wearable device control has been neglected so far. Thus, this book fills a gap in the literature providing readers with extensive information and a source of inspiration for the future design and control of medical and assistive devices.

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