Literature DB >> 15255778

Robotics, motor learning, and neurologic recovery.

David J Reinkensmeyer1, Jeremy L Emken, Steven C Cramer.   

Abstract

Robotic devices are helping shed light on human motor control in health and injury. By using robots to apply novel force fields to the arm, investigators are gaining insight into how the nervous system models its external dynamic environment. The nervous system builds internal models gradually by experience and uses them in combination with impedance and feedback control strategies. Internal models are robust to environmental and neural noise, generalized across space, implemented in multiple brain regions, and developed in childhood. Robots are also being used to assist in repetitive movement practice following neurologic injury, providing insight into movement recovery. Robots can haptically assess sensorimotor performance, administer training, quantify amount of training, and improve motor recovery. In addition to providing insight into motor control, robotic paradigms may eventually enhance motor learning and rehabilitation beyond the levels possible with conventional training techniques.

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Year:  2004        PMID: 15255778     DOI: 10.1146/annurev.bioeng.6.040803.140223

Source DB:  PubMed          Journal:  Annu Rev Biomed Eng        ISSN: 1523-9829            Impact factor:   9.590


  70 in total

1.  Electroencephalographic reactivity to unimodal and bimodal visual and proprioceptive demands in sensorimotor integration.

Authors:  J C Mizelle; Larry Forrester; Mark Hallett; Lewis A Wheaton
Journal:  Exp Brain Res       Date:  2010-05-06       Impact factor: 1.972

2.  Motion controlled gait enhancing mobile shoe for rehabilitation.

Authors:  Ismet Handzic; Erin V Vasudevan; Kyle B Reed
Journal:  IEEE Int Conf Rehabil Robot       Date:  2011

3.  Short-Duration and Intensive Training Improves Long-Term Reaching Performance in Individuals With Chronic Stroke.

Authors:  Hyeshin Park; Sujin Kim; Carolee J Winstein; James Gordon; Nicolas Schweighofer
Journal:  Neurorehabil Neural Repair       Date:  2015-09-24       Impact factor: 3.919

4.  Powered lower limb orthoses for gait rehabilitation.

Authors:  Daniel P Ferris; Gregory S Sawicki; Antoinette Domingo
Journal:  Top Spinal Cord Inj Rehabil       Date:  2005

5.  Recumbent stepping has similar but simpler neural control compared to walking.

Authors:  Rebecca H Stoloff; E Paul Zehr; Daniel P Ferris
Journal:  Exp Brain Res       Date:  2006-10-27       Impact factor: 1.972

6.  A Semi-passive Planar Manipulandum for Upper-Extremity Rehabilitation.

Authors:  Chih-Kang Chang; Edward P Washabaugh; Andrew Gwozdziowski; C David Remy; Chandramouli Krishnan
Journal:  Ann Biomed Eng       Date:  2018-04-06       Impact factor: 3.934

7.  Reach adaptation: what determines whether we learn an internal model of the tool or adapt the model of our arm?

Authors:  JoAnn Kluzik; Jörn Diedrichsen; Reza Shadmehr; Amy J Bastian
Journal:  J Neurophysiol       Date:  2008-07-02       Impact factor: 2.714

8.  Incorporating haptic effects into three-dimensional virtual environments to train the hemiparetic upper extremity.

Authors:  Sergei V Adamovich; Gerard G Fluet; Alma S Merians; Abraham Mathai; Qinyin Qiu
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2009-08-07       Impact factor: 3.802

Review 9.  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

10.  Effects of intensive arm training with the rehabilitation robot ARMin II in chronic stroke patients: four single-cases.

Authors:  Patricia Staubli; Tobias Nef; Verena Klamroth-Marganska; Robert Riener
Journal:  J Neuroeng Rehabil       Date:  2009-12-17       Impact factor: 4.262

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