Literature DB >> 21763518

Physically interactive robotic technology for neuromotor rehabilitation.

Neville Hogan1, Hermano I Krebs.   

Abstract

Robotic technology can provide innovative responses to the severe challenges of providing cost-effective care to restore sensory-motor function following neurological and biomechanical injury. It may be deployed at several points on a continuum of care, to provide precisely controlled sensory-motor therapy to ameliorate disability and promote recovery of function, or to provide assistance to compensate for functions that cannot be recovered, or to replace limbs lost irretrievably. This chapter reviews recent progress using robotic technology to capitalize on neural plasticity and promote recovery after neurological injury such as stroke (cerebral vascular accident), research on brain-computer interfaces as a source of control signals for assistive technologies, and research on high-performance multiple-degree-of-freedom upper-extremity prosthetic limbs.
Copyright © 2011 Elsevier B.V. All rights reserved.

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Year:  2011        PMID: 21763518     DOI: 10.1016/B978-0-444-53355-5.00004-X

Source DB:  PubMed          Journal:  Prog Brain Res        ISSN: 0079-6123            Impact factor:   2.453


  11 in total

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Authors:  Eugene N Muratov; Jürgen Bajorath; Robert P Sheridan; Igor V Tetko; Dmitry Filimonov; Vladimir Poroikov; Tudor I Oprea; Igor I Baskin; Alexandre Varnek; Adrian Roitberg; Olexandr Isayev; Stefano Curtarolo; Denis Fourches; Yoram Cohen; Alan Aspuru-Guzik; David A Winkler; Dimitris Agrafiotis; Artem Cherkasov; Alexander Tropsha
Journal:  Chem Soc Rev       Date:  2020-05-01       Impact factor: 54.564

2.  Self-powered robots to reduce motor slacking during upper-extremity rehabilitation: a proof of concept study.

Authors:  Edward P Washabaugh; Emma Treadway; R Brent Gillespie; C David Remy; Chandramouli Krishnan
Journal:  Restor Neurol Neurosci       Date:  2018       Impact factor: 2.406

3.  Translating concepts of neural repair after stroke: Structural and functional targets for recovery.

Authors:  Robert W Regenhardt; Hajime Takase; Eng H Lo; David J Lin
Journal:  Restor Neurol Neurosci       Date:  2020       Impact factor: 2.406

4.  Decoding continuous limb movements from high-density epidural electrode arrays using custom spatial filters.

Authors:  A R Marathe; D M Taylor
Journal:  J Neural Eng       Date:  2013-04-23       Impact factor: 5.379

5.  Detection of self-paced reaching movement intention from EEG signals.

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Journal:  Front Neuroeng       Date:  2012-07-12

6.  Multimodal movement prediction - towards an individual assistance of patients.

Authors:  Elsa Andrea Kirchner; Marc Tabie; Anett Seeland
Journal:  PLoS One       Date:  2014-01-08       Impact factor: 3.240

7.  Sitting and standing intention can be decoded from scalp EEG recorded prior to movement execution.

Authors:  Thomas C Bulea; Saurabh Prasad; Atilla Kilicarslan; Jose L Contreras-Vidal
Journal:  Front Neurosci       Date:  2014-11-25       Impact factor: 4.677

8.  A future without chronic pain: neuroscience and clinical research.

Authors:  David Borsook
Journal:  Cerebrum       Date:  2012-06-27

9.  Kinematic and neurophysiological consequences of an assisted-force-feedback brain-machine interface training: a case study.

Authors:  Stefano Silvoni; Marianna Cavinato; Chiara Volpato; Giulia Cisotto; Clara Genna; Michela Agostini; Andrea Turolla; Ander Ramos-Murguialday; Francesco Piccione
Journal:  Front Neurol       Date:  2013-11-07       Impact factor: 4.003

10.  A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction.

Authors:  Hendrik Wöhrle; Marc Tabie; Su Kyoung Kim; Frank Kirchner; Elsa Andrea Kirchner
Journal:  Sensors (Basel)       Date:  2017-07-03       Impact factor: 3.576

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