Literature DB >> 22481803

Augmented dynamics and motor exploration as training for stroke.

Felix C Huang1, James L Patton.   

Abstract

With chronic stroke survivors (n = 30), we investigated how upper extremity training with negative viscosity affects coordination under unperturbed conditions. Subjects trained with a planar robotic interface simulating 1) negative viscosity augmented to elbow and shoulder joints; 2) negative viscosity combined with inertia; or 3) a null-field condition. Two treatment groups practiced with both force conditions (cross-over design), while a control group practiced with a null-field condition. Training (exploratory movement) and evaluations (prescribed circular movement) alternated in several phases to facilitate transfer from forces to the null field. Negative viscosity expanded exploration especially in the sagittal axis, and resulted in significant within-day improvements. Both treatment groups exhibited next day retention unobserved in the control. Our results suggest enhanced learning from forces that induce a broader range of kinematics. This study supports the use of robot-assisted training that encourages active patient involvement by preserving efferent commands for driving movement.

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Mesh:

Year:  2012        PMID: 22481803      PMCID: PMC4914037          DOI: 10.1109/TBME.2012.2192116

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  33 in total

1.  Composition and decomposition of internal models in motor learning under altered kinematic and dynamic environments.

Authors:  J R Flanagan; E Nakano; H Imamizu; R Osu; T Yoshioka; M Kawato
Journal:  J Neurosci       Date:  1999-10-15       Impact factor: 6.167

2.  Kinematics and dynamics are not represented independently in motor working memory: evidence from an interference study.

Authors:  Christine Tong; Daniel M Wolpert; J Randall Flanagan
Journal:  J Neurosci       Date:  2002-02-01       Impact factor: 6.167

3.  Motor learning elicited by voluntary drive.

Authors:  Martin Lotze; Christoph Braun; Niels Birbaumer; Silke Anders; Leonardo G Cohen
Journal:  Brain       Date:  2003-04       Impact factor: 13.501

4.  Robot-assisted adaptive training: custom force fields for teaching movement patterns.

Authors:  James L Patton; Ferdinando A Mussa-Ivaldi
Journal:  IEEE Trans Biomed Eng       Date:  2004-04       Impact factor: 4.538

5.  Robot-enhanced motor learning: accelerating internal model formation during locomotion by transient dynamic amplification.

Authors:  Jeremy L Emken; David J Reinkensmeyer
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2005-03       Impact factor: 3.802

6.  Modular decomposition in visuomotor learning.

Authors:  Z Ghahramani; D M Wolpert
Journal:  Nature       Date:  1997-03-27       Impact factor: 49.962

Review 7.  Robot-assisted movement training for the stroke-impaired arm: Does it matter what the robot does?

Authors:  Leonard E Kahn; Peter S Lum; W Zev Rymer; David J Reinkensmeyer
Journal:  J Rehabil Res Dev       Date:  2006 Aug-Sep

8.  Effects of cognitive processes and task complexity on acquisition, retention, and transfer of motor skills.

Authors:  T Jarus; T Gutman
Journal:  Can J Occup Ther       Date:  2001-12       Impact factor: 1.614

9.  The transition to reaching: mapping intention and intrinsic dynamics.

Authors:  E Thelen; D Corbetta; K Kamm; J P Spencer; K Schneider; R F Zernicke
Journal:  Child Dev       Date:  1993-08

Review 10.  Gait training strategies utilized in poststroke rehabilitation: are we really making a difference?

Authors:  Ross Bogey; George T Hornby
Journal:  Top Stroke Rehabil       Date:  2007 Nov-Dec       Impact factor: 2.119

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  12 in total

1.  Sonification and haptic feedback in addition to visual feedback enhances complex motor task learning.

Authors:  Roland Sigrist; Georg Rauter; Laura Marchal-Crespo; Robert Riener; Peter Wolf
Journal:  Exp Brain Res       Date:  2014-12-16       Impact factor: 1.972

2.  Variable Damping Force Tunnel for Gait Training Using ALEX III.

Authors:  Paul Stegall; Damiano Zanotto; Sunil K Agrawal
Journal:  IEEE Robot Autom Lett       Date:  2017-02-17

3.  Simulation of variable impedance as an intervention for upper extremity motor exploration.

Authors:  Felix C Huang
Journal:  IEEE Int Conf Rehabil Robot       Date:  2017-07

4.  Movement distributions of stroke survivors exhibit distinct patterns that evolve with training.

Authors:  Felix C Huang; James L Patton
Journal:  J Neuroeng Rehabil       Date:  2016-03-09       Impact factor: 4.262

5.  The effects of error-augmentation versus error-reduction paradigms in robotic therapy to enhance upper extremity performance and recovery post-stroke: a systematic review.

Authors:  Le Yu Liu; Youlin Li; Anouk Lamontagne
Journal:  J Neuroeng Rehabil       Date:  2018-07-04       Impact factor: 4.262

6.  Energetics during robot-assisted training predicts recovery in stroke.

Authors:  Zachary A Wright; James L Patton; Felix C Huang
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2018-07

7.  Viscous field training induces after effects but hinders recovery of overground locomotion following spinal cord injury in rats.

Authors:  Nathan D Neckel; Haining Dai
Journal:  Behav Brain Res       Date:  2021-06-18       Impact factor: 3.352

8.  Effect of Position- and Velocity-Dependent Forces on Reaching Movements at Different Speeds.

Authors:  Susanna Summa; Maura Casadio; Vittorio Sanguineti
Journal:  Front Hum Neurosci       Date:  2016-11-29       Impact factor: 3.169

9.  Reorganization of finger coordination patterns through motor exploration in individuals after stroke.

Authors:  Rajiv Ranganathan
Journal:  J Neuroeng Rehabil       Date:  2017-09-11       Impact factor: 4.262

10.  Patient-Active Control of a Powered Exoskeleton Targeting Upper Limb Rehabilitation Training.

Authors:  Qingcong Wu; Xingsong Wang; Bai Chen; Hongtao Wu
Journal:  Front Neurol       Date:  2018-10-11       Impact factor: 4.003

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