Literature DB >> 18772279

Computerized visual feedback: an adjunct to robotic-assisted gait training.

Raphael Banz1, Marc Bolliger, Gery Colombo, Volker Dietz, Lars Lünenburger.   

Abstract

BACKGROUND AND
PURPOSE: Robotic devices for walking rehabilitation allow new possibilities for providing performance-related information to patients during gait training. Based on motor learning principles, augmented feedback during robotic-assisted gait training might improve the rehabilitation process used to regain walking function. This report presents a method to provide visual feedback implemented in a driven gait orthosis (DGO). The purpose of the study was to compare the immediate effect on motor output in subjects during robotic-assisted gait training when they used computerized visual feedback and when they followed verbal instructions of a physical therapist.
SUBJECTS: Twelve people with neurological gait disorders due to incomplete spinal cord injury participated.
METHODS: Subjects were instructed to walk within the DGO in 2 different conditions. They were asked to increase their motor output by following the instructions of a therapist and by observing visual feedback. In addition, the subjects' opinions about using visual feedback were investigated by a questionnaire.
RESULTS: Computerized visual feedback and verbal instructions by the therapist were observed to result in a similar change in motor output in subjects when walking within the DGO. Subjects reported that they were more motivated and concentrated on their movements when using computerized visual feedback compared with when no form of feedback was provided. DISCUSSION AND
CONCLUSION: Computerized visual feedback is a valuable adjunct to robotic-assisted gait training. It represents a relevant tool to increase patients' motor output, involvement, and motivation during gait training, similar to verbal instructions by a therapist.

Entities:  

Mesh:

Year:  2008        PMID: 18772279     DOI: 10.2522/ptj.20070203

Source DB:  PubMed          Journal:  Phys Ther        ISSN: 0031-9023


  19 in total

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2.  Age is negatively associated with upper limb recovery after conventional but not robotic rehabilitation in patients with stroke: a secondary analysis of a randomized-controlled trial.

Authors:  Francesca Cecchi; Marco Germanotta; Claudio Macchi; Angelo Montesano; Silvia Galeri; Manuela Diverio; Catiuscia Falsini; Monica Martini; Rita Mosca; Emanuele Langone; Dionysia Papadopoulou; Maria Chiara Carrozza; Irene Aprile
Journal:  J Neurol       Date:  2020-08-25       Impact factor: 4.849

3.  Influence of virtual reality soccer game on walking performance in robotic assisted gait training for children.

Authors:  Karin Brütsch; Tabea Schuler; Alexander Koenig; Lukas Zimmerli; Susan Mérillat -Koeneke; Lars Lünenburger; Robert Riener; Lutz Jäncke; Andreas Meyer-Heim
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4.  Allowing intralimb kinematic variability during locomotor training poststroke improves kinematic consistency: a subgroup analysis from a randomized clinical trial.

Authors:  Michael D Lewek; Theresa H Cruz; Jennifer L Moore; Heidi R Roth; Yasin Y Dhaher; T George Hornby
Journal:  Phys Ther       Date:  2009-06-11

5.  Effects of Robotic Neurorehabilitation on Body Representation in Individuals with Stroke: A Preliminary Study Focusing on an EEG-Based Approach.

Authors:  Maria Grazia Maggio; Antonino Naro; Alfredo Manuli; Giuseppa Maresca; Tina Balletta; Desirèe Latella; Rosaria De Luca; Rocco Salvatore Calabrò
Journal:  Brain Topogr       Date:  2021-03-04       Impact factor: 3.020

6.  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

7.  Effect of visual distraction and auditory feedback on patient effort during robot-assisted movement training after stroke.

Authors:  Riccardo Secoli; Marie-Helene Milot; Giulio Rosati; David J Reinkensmeyer
Journal:  J Neuroeng Rehabil       Date:  2011-04-23       Impact factor: 4.262

8.  A Portable Gait Asymmetry Rehabilitation System for Individuals with Stroke Using a Vibrotactile Feedback.

Authors:  Muhammad Raheel Afzal; Min-Kyun Oh; Chang-Hee Lee; Young Sook Park; Jungwon Yoon
Journal:  Biomed Res Int       Date:  2015-06-16       Impact factor: 3.411

9.  Active robotic training improves locomotor function in a stroke survivor.

Authors:  Chandramouli Krishnan; Rajiv Ranganathan; Shailesh S Kantak; Yasin Y Dhaher; William Z Rymer
Journal:  J Neuroeng Rehabil       Date:  2012-08-20       Impact factor: 4.262

10.  Selective control of gait subtasks in robotic gait training: foot clearance support in stroke survivors with a powered exoskeleton.

Authors:  Bram Koopman; Edwin H F van Asseldonk; Herman van der Kooij
Journal:  J Neuroeng Rehabil       Date:  2013-01-21       Impact factor: 4.262

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