Literature DB >> 15502275

Interactive robots for neuro-rehabilitation.

Neville Hogan1, Hermano I Krebs.   

Abstract

This article reviews a decade of work aimed at developing effective interactive robotic tools to treat and understand motor impairment and disability. The success of an initial pilot study with acute-phase in-patients recovering from stroke prompted a larger study showing that these results could be replicated and a follow-up study showing that the benefits lasted. Studies of chronic-phase out-patients demonstrated that similar benefits could be obtained which also lasted and were accompanied by a concomitant reduction of pain. Exploration of the likely biology of recovery suggested an improvement of robotic treatment in the form of performance-based progressive therapy aimed at accelerating a process akin to motor learning postulated to underlie recovery. Initial studies of this method show a dramatic improvement over the previous successes. Kinematic studies of the recovery process show that, similar to the development of motor behavior in infants, it begins with stereotyped submovements and proceeds by progressively merging these to approach unimpaired motor performance.

Entities:  

Mesh:

Year:  2004        PMID: 15502275

Source DB:  PubMed          Journal:  Restor Neurol Neurosci        ISSN: 0922-6028            Impact factor:   2.406


  18 in total

1.  Implications of assist-as-needed robotic step training after a complete spinal cord injury on intrinsic strategies of motor learning.

Authors:  Lance L Cai; Andy J Fong; Chad K Otoshi; Yongqiang Liang; Joel W Burdick; Roland R Roy; V Reggie Edgerton
Journal:  J Neurosci       Date:  2006-10-11       Impact factor: 6.167

2.  Normalized movement quality measures for therapeutic robots strongly correlate with clinical motor impairment measures.

Authors:  Ozkan Celik; Marcia K O'Malley; Corwin Boake; Harvey S Levin; Nuray Yozbatiran; Timothy A Reistetter
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2010-04-12       Impact factor: 3.802

Review 3.  Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review.

Authors:  Gert Kwakkel; Boudewijn J Kollen; Hermano I Krebs
Journal:  Neurorehabil Neural Repair       Date:  2007-09-17       Impact factor: 3.919

4.  Uncertainty of feedback and state estimation determines the speed of motor adaptation.

Authors:  Kunlin Wei; Konrad Körding
Journal:  Front Comput Neurosci       Date:  2010-05-11       Impact factor: 2.380

5.  A system for delivering mechanical stimulation and robot-assisted therapy to the rat whisker pad during facial nerve regeneration.

Authors:  James T Heaton; Christopher J Knox; Juan S Malo; James B Kobler; Tessa A Hadlock
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2013-03-07       Impact factor: 3.802

6.  Neuromuscular and biomechanical factors codetermine the solution to motor redundancy in rhythmic multijoint arm movement.

Authors:  Aymar de Rugy; Stephan Riek; Yalchin Oytam; Timothy J Carroll; Rahman Davoodi; Richard G Carson
Journal:  Exp Brain Res       Date:  2008-06-11       Impact factor: 1.972

7.  Relevance of error: what drives motor adaptation?

Authors:  Kunlin Wei; Konrad Körding
Journal:  J Neurophysiol       Date:  2008-11-19       Impact factor: 2.714

8.  A pelvic implant orthosis in rodents, for spinal cord injury rehabilitation, and for brain machine interface research: construction, surgical implantation and validation.

Authors:  Ubong Ime Udoekwere; Chintan S Oza; Simon F Giszter
Journal:  J Neurosci Methods       Date:  2013-11-19       Impact factor: 2.390

9.  A Pre-Clinical Framework for Neural Control of a Therapeutic Upper-Limb Exoskeleton.

Authors:  Amy Blank; Marcia K O'Malley; Gerard E Francisco; Jose L Contreras-Vidal
Journal:  Int IEEE EMBS Conf Neural Eng       Date:  2013

Review 10.  Review of control strategies for robotic movement training after neurologic injury.

Authors:  Laura Marchal-Crespo; David J Reinkensmeyer
Journal:  J Neuroeng Rehabil       Date:  2009-06-16       Impact factor: 4.262

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