Literature DB >> 27336075

Short-term Performance-based Error-augmentation versus Error-reduction Robotic Gait Training for Individuals with Chronic Stroke: A Pilot Study.

P C Kao1, S Srivastava2, J S Higginson3, S K Agrawal4, J P Scholz5.   

Abstract

The success of locomotion training with robotic exoskeletons requires identifying control algorithms that effectively retrain gait patterns in neurologically impaired individuals. Here we report how the two training paradigms, performance-based error-augmentation versus error-reduction, modified walking patterns in four chronic post-stroke individuals as a proof-of-concept for future locomotion training following stroke. Stroke subjects were instructed to match a prescribed walking pattern template derived from neurologically intact individuals. Target templates based on the spatial paths of lateral ankle malleolus positions during walking were created for each subject. Robotic forces were applied that either decreased (error-reduction) or increased (error-augmentation) the deviation between subjects' instantaneous malleolus positions and their target template. Subjects' performance was quantified by the amount of deviation between their actual and target malleolus paths. After the error-reduction training, S1 showed a malleolus path with reduced deviation from the target template by 16%. In contrast, S4 had a malleolus path further away from the template with increased deviation by 12%. After the error-augmentation training, S2 had a malleolus path greatly approximating the template with reduced deviation by 58% whereas S3 walked with higher steps than his baseline with increased deviation by 37%. These findings suggest that an error-reduction force field has minimal effects on modifying subject's gait patterns whereas an error-augmentation force field may promote a malleolus path either approximating or exceeding the target walking template. Future investigation will need to evaluate the long-term training effects on over-ground walking and functional capacity.

Entities:  

Keywords:  Force field; Gait rehabilitation; Rehabilitation robotics; Stroke; Walking

Year:  2015        PMID: 27336075      PMCID: PMC4914051     

Source DB:  PubMed          Journal:  Phys Med Rehabil Int        ISSN: 2471-0377


  26 in total

1.  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

2.  Robotic-assisted step training (lokomat) not superior to equal intensity of over-ground rehabilitation in patients with multiple sclerosis.

Authors:  Claude Vaney; Brigitte Gattlen; Véronique Lugon-Moulin; André Meichtry; Rita Hausammann; Denise Foinant; Anne-Marie Anchisi-Bellwald; Cécilia Palaci; Roger Hilfiker
Journal:  Neurorehabil Neural Repair       Date:  2011-12-02       Impact factor: 3.919

3.  Design of a minimally constraining, passively supported gait training exoskeleton: ALEX II.

Authors:  Kyle N Winfree; Paul Stegall; Sunil K Agrawal
Journal:  IEEE Int Conf Rehabil Robot       Date:  2011

4.  Enhanced gait-related improvements after therapist- versus robotic-assisted locomotor training in subjects with chronic stroke: a randomized controlled study.

Authors:  T George Hornby; Donielle D Campbell; Jennifer H Kahn; Tobey Demott; Jennifer L Moore; Heidi R Roth
Journal:  Stroke       Date:  2008-05-08       Impact factor: 7.914

5.  Effects of complementary auditory feedback in robot-assisted lower extremity motor adaptation.

Authors:  Damiano Zanotto; Giulio Rosati; Simone Spagnol; Paul Stegall; Sunil K Agrawal
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2013-03-18       Impact factor: 3.802

6.  Robotic resistance treadmill training improves locomotor function in human spinal cord injury: a pilot study.

Authors:  Ming Wu; Jill M Landry; Brian D Schmit; T George Hornby; Sheng-Che Yen
Journal:  Arch Phys Med Rehabil       Date:  2012-03-27       Impact factor: 3.966

7.  Classification of walking handicap in the stroke population.

Authors:  J Perry; M Garrett; J K Gronley; S J Mulroy
Journal:  Stroke       Date:  1995-06       Impact factor: 7.914

Review 8.  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

9.  Controlling patient participation during robot-assisted gait training.

Authors:  Alexander Koenig; Ximena Omlin; Jeannine Bergmann; Lukas Zimmerli; Marc Bolliger; Friedemann Müller; Robert Riener
Journal:  J Neuroeng Rehabil       Date:  2011-03-23       Impact factor: 4.262

10.  A pilot study on the feasibility of robot-aided leg motor training to facilitate active participation.

Authors:  Chandramouli Krishnan; Rajiv Ranganathan; Yasin Y Dhaher; William Z Rymer
Journal:  PLoS One       Date:  2013-10-11       Impact factor: 3.240

View more
  2 in total

1.  Interlimb transfer of motor skill learning during walking: No evidence for asymmetric transfer.

Authors:  Chandramouli Krishnan; Rajiv Ranganathan; Manik Tetarbe
Journal:  Gait Posture       Date:  2017-04-27       Impact factor: 2.840

2.  Improving Precision Force Control With Low-Frequency Error Amplification Feedback: Behavioral and Neurophysiological Mechanisms.

Authors:  Ing-Shiou Hwang; Chia-Ling Hu; Zong-Ru Yang; Yen-Ting Lin; Yi-Ching Chen
Journal:  Front Physiol       Date:  2019-02-20       Impact factor: 4.566

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.