Literature DB >> 18988916

Effects of training with a robot-virtual reality system compared with a robot alone on the gait of individuals after stroke.

Anat Mirelman1, Paolo Bonato, Judith E Deutsch.   

Abstract

BACKGROUND AND
PURPOSE: Training of the lower extremity (LE) using a robot coupled with virtual environments has shown to transfer to improved overground locomotion. The purpose of this study was to determine whether the transfer of training of LE movements to locomotion was greater using a virtual environment coupled with a robot or with the robot alone.
METHODS: A single, blind, randomized clinical trial was conducted. Eighteen individuals poststroke participated in a 4-week training protocol. One group trained with the robot virtual reality (VR) system and the other group trained with the robot alone. Outcome measures were temporal features of gait measured in a laboratory setting and the community.
RESULTS: Greater changes in velocity and distance walked were demonstrated for the group trained with the robotic device coupled with the VR than training with the robot alone. Similarly, significantly greater improvements in the distance walked and number of steps taken in the community were measured for the group that trained with robot coupled with the VR. These differences were maintained at 3 months' follow-up.
CONCLUSIONS: The study is the first to demonstrate that LE training of individuals with chronic hemiparesis using a robotic device coupled with VR improved walking ability in the laboratory and the community better than robot training alone.

Entities:  

Mesh:

Year:  2008        PMID: 18988916     DOI: 10.1161/STROKEAHA.108.516328

Source DB:  PubMed          Journal:  Stroke        ISSN: 0039-2499            Impact factor:   7.914


  73 in total

1.  A paradox: after stroke, the non-lesioned lower limb motor cortex may be maladaptive.

Authors:  Sangeetha Madhavan; Lynn M Rogers; James W Stinear
Journal:  Eur J Neurosci       Date:  2010-08-16       Impact factor: 3.386

2.  Robotics and gaming to improve ankle strength, motor control, and function in children with cerebral palsy--a case study series.

Authors:  Grigore C Burdea; Daniel Cioi; Angad Kale; William E Janes; Sandy A Ross; Jack R Engsberg
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2012-07-03       Impact factor: 3.802

3.  A comparison of motor adaptations to robotically facilitated upper extremity task practice demonstrated by children with cerebral palsy and adults with stroke.

Authors:  Qinyin Qiu; Sergei Adamovich; Soha Saleh; Ian Lafond; Alma S Merians; Gerard G Fluet
Journal:  IEEE Int Conf Rehabil Robot       Date:  2011

4.  Combining Fast-Walking Training and a Step Activity Monitoring Program to Improve Daily Walking Activity After Stroke: A Preliminary Study.

Authors:  Kelly A Danks; Ryan Pohlig; Darcy S Reisman
Journal:  Arch Phys Med Rehabil       Date:  2016-05-27       Impact factor: 3.966

5.  Robotic Assistance for Training Finger Movement Using a Hebbian Model: A Randomized Controlled Trial.

Authors:  Justin B Rowe; Vicky Chan; Morgan L Ingemanson; Steven C Cramer; Eric T Wolbrecht; David J Reinkensmeyer
Journal:  Neurorehabil Neural Repair       Date:  2017-08       Impact factor: 3.919

6.  Immersive virtual reality improves movement patterns in patients after ACL reconstruction: implications for enhanced criteria-based return-to-sport rehabilitation.

Authors:  Alli Gokeler; Marsha Bisschop; Gregory D Myer; Anne Benjaminse; Pieter U Dijkstra; Helco G van Keeken; Jos J A M van Raay; Johannes G M Burgerhof; Egbert Otten
Journal:  Knee Surg Sports Traumatol Arthrosc       Date:  2014-10-14       Impact factor: 4.342

Review 7.  Sensorimotor training in virtual reality: a review.

Authors:  Sergei V Adamovich; Gerard G Fluet; Eugene Tunik; Alma S Merians
Journal:  NeuroRehabilitation       Date:  2009       Impact factor: 2.138

Review 8.  Technological advances in interventions to enhance poststroke gait.

Authors:  Lynne R Sheffler; John Chae
Journal:  Phys Med Rehabil Clin N Am       Date:  2013-05       Impact factor: 1.784

9.  Reinforcing Motor Re-Training and Rehabilitation through Games: A Machine-Learning Perspective.

Authors:  Maurizio Schmid
Journal:  Front Neuroeng       Date:  2009-03-31

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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