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

By: Contributor(s): Material type: TextTextLanguage: 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:
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 Home library Collection Call number Copy number Status Notes Date due Barcode
E-Book 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 BMM EBK03385

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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