Literature DB >> 28813810

Dissociating two sources of variability using a safety-margin model.

Amit K Shah, James L Patton.   

Abstract

Neurological trauma can have a devastating effect on activities of daily living. One of the consequences is an increased amount of variability in the system, which can challenge individuals to stay within safe and stable regions of operation. There are multiple sources of movement variability; two of these are neuromotor noise and action-tolerance variability. The amount of neuromotor noise that is uncontrollable can impose limitations on reshaping variability. Action-tolerance variability, which can be reshaped through experience, and neuromotor noise, a certain amount of which cannot be altered, are often conflated when discussing motor variability. We attempted to disambiguate the two using an adaptive model, producing distinct "signatures" of neuromotor noise and action-tolerance variability within a task and compare with experimental data on stroke and healthy. Not all stroke survivors could adapt to the task, as predicted for those with greater neuromotor noise. Possible applications of this model can inform us of potential to influence distributions in stroke survivors and other individuals who have had a neurological injury. Additionally, we could design new training environments specifically tailored to the needs of the individual. This technique may also help disambiguate the type of brain injury suffered by stroke survivors.

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Mesh:

Year:  2017        PMID: 28813810      PMCID: PMC8811716          DOI: 10.1109/ICORR.2017.8009238

Source DB:  PubMed          Journal:  IEEE Int Conf Rehabil Robot        ISSN: 1945-7898


  18 in total

1.  Relative stability improves with experience in a dynamic standing task.

Authors:  J L Patton; W A Lee; Y C Pai
Journal:  Exp Brain Res       Date:  2000-11       Impact factor: 1.972

2.  Obstacle avoidance during online corrections.

Authors:  Craig S Chapman; Melvyn A Goodale
Journal:  J Vis       Date:  2010-09-22       Impact factor: 2.240

Review 3.  Neuronal variability: noise or part of the signal?

Authors:  Richard B Stein; E Roderich Gossen; Kelvin E Jones
Journal:  Nat Rev Neurosci       Date:  2005-05       Impact factor: 34.870

4.  Improvement of arm movement patterns and endpoint control depends on type of feedback during practice in stroke survivors.

Authors:  M C Cirstea; M F Levin
Journal:  Neurorehabil Neural Repair       Date:  2007-03-16       Impact factor: 3.919

5.  Energy margins in dynamic object manipulation.

Authors:  Christopher J Hasson; Tian Shen; Dagmar Sternad
Journal:  J Neurophysiol       Date:  2012-05-16       Impact factor: 2.714

6.  Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors.

Authors:  James L Patton; Mary Ellen Stoykov; Mark Kovic; Ferdinando A Mussa-Ivaldi
Journal:  Exp Brain Res       Date:  2005-10-26       Impact factor: 1.972

7.  Consequences of increased neuromotor noise for reaching movements in persons with stroke.

Authors:  Patrick H McCrea; Janice J Eng
Journal:  Exp Brain Res       Date:  2004-11-05       Impact factor: 1.972

Review 8.  Robotics and virtual reality: a perfect marriage for motor control research and rehabilitation.

Authors:  James Patton; Greg Dawe; Chris Scharver; Ferdinando Mussa-Ivaldi; Robert Kenyon
Journal:  Assist Technol       Date:  2006

9.  Neuromotor noise limits motor performance, but not motor adaptation, in children.

Authors:  Craig D Takahashi; Dan Nemet; Christie M Rose-Gottron; Jennifer K Larson; Dan M Cooper; David J Reinkensmeyer
Journal:  J Neurophysiol       Date:  2003-08       Impact factor: 2.714

10.  Movement variability in stroke patients and controls performing two upper limb functional tasks: a new assessment methodology.

Authors:  Sibylle B Thies; Phil A Tresadern; Laurence P Kenney; Joel Smith; David Howard; John Y Goulermas; Christine Smith; Julie Rigby
Journal:  J Neuroeng Rehabil       Date:  2009-01-23       Impact factor: 4.262

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