sEMG-based Control Strategy for a Hand Exoskeleton System [electronic resource] / by Nicola Secciani.

By:

Secciani, Nicola [author.]

Contributor(s):

SpringerLink (Online service)

Material type: Text

TextLanguage: İngilizce Series: Springer Theses, Recognizing Outstanding Ph.D. ResearchPublisher: Cham : Springer International Publishing : Imprint: Springer, 2022Edition: 1st ed. 2022Description: 1 online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783030902834

Subject(s):

NLM classification:

WE 26

Online resources:

Springer eBooks Online access link to the resource

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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Item type	Current library	Home library	Collection	Call number	Copy number	Status	Date due	Barcode
E-Book	Tıp Fakültesi Medikal Kütüphane	Tıp Fakültesi Medikal Kütüphane	E-Kitap Koleksiyonu	WE 26EBK (Browse shelf(Opens below))	1	Geçerli değil-e-Kitap / Not applicable-e-Book		EBK03385

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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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