Literature DB >> 21216650

Robot assisted treadmill training: mechanisms and training strategies.

Shahid Hussain1, Sheng Quan Xie, Guangyu Liu.   

Abstract

The rehabilitation engineering community is working towards the development of robotic devices that can assist during gait training of patients suffering from neurologic injuries such as stroke and spinal cord injuries (SCI). The field of robot assisted treadmill training has rapidly evolved during the last decade. The robotic devices can provide repetitive, systematic and prolonged gait training sessions. This paper presents a review of the treadmill based robotic gait training devices. An overview of design configurations and actuation methods used for these devices is provided. Training strategies designed to actively involve the patient in robot assisted treadmill training are studied. These training strategies assist the patient according to the level of disability and type of neurologic injury. Although the efficacy of these training strategies is not clinically proven, adaptive strategies may result in substantial improvements. We end our review with a discussion covering major advancements made at device design and training strategies level and potential challenges to the field.
Copyright © 2010 IPEM. Published by Elsevier Ltd. All rights reserved.

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Year:  2011        PMID: 21216650     DOI: 10.1016/j.medengphy.2010.12.010

Source DB:  PubMed          Journal:  Med Eng Phys        ISSN: 1350-4533            Impact factor:   2.242


  17 in total

1.  Trunk robot rehabilitation training with active stepping reorganizes and enriches trunk motor cortex representations in spinal transected rats.

Authors:  Chintan S Oza; Simon F Giszter
Journal:  J Neurosci       Date:  2015-05-06       Impact factor: 6.167

Review 2.  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

3.  Plasticity and alterations of trunk motor cortex following spinal cord injury and non-stepping robot and treadmill training.

Authors:  Chintan S Oza; Simon F Giszter
Journal:  Exp Neurol       Date:  2014-04-03       Impact factor: 5.330

4.  The H2 robotic exoskeleton for gait rehabilitation after stroke: early findings from a clinical study.

Authors:  Magdo Bortole; Anusha Venkatakrishnan; Fangshi Zhu; Juan C Moreno; Gerard E Francisco; Jose L Pons; Jose L Contreras-Vidal
Journal:  J Neuroeng Rehabil       Date:  2015-06-17       Impact factor: 4.262

5.  Kinematics of turning during walking over ground and on a rotating treadmill.

Authors:  Janez Pavčič; Zlatko Matjačić; Andrej Olenšek
Journal:  J Neuroeng Rehabil       Date:  2014-08-23       Impact factor: 4.262

6.  Volition-adaptive control for gait training using wearable exoskeleton: preliminary tests with incomplete spinal cord injury individuals.

Authors:  Vijaykumar Rajasekaran; Eduardo López-Larraz; Fernando Trincado-Alonso; Joan Aranda; Luis Montesano; Antonio J Del-Ama; Jose L Pons
Journal:  J Neuroeng Rehabil       Date:  2018-01-03       Impact factor: 4.262

7.  An Open-Structure Treadmill Gait Trainer: From Research to Application.

Authors:  Jian Li; Diansheng Chen; Yubo Fan
Journal:  J Healthc Eng       Date:  2017-06-15       Impact factor: 2.682

8.  Differences in muscle activity and temporal step parameters between Lokomat guided walking and treadmill walking in post-stroke hemiparetic patients and healthy walkers.

Authors:  Klaske van Kammen; Anne M Boonstra; Lucas H V van der Woude; Heleen A Reinders-Messelink; Rob den Otter
Journal:  J Neuroeng Rehabil       Date:  2017-04-20       Impact factor: 4.262

9.  Effects of robotic guidance on the coordination of locomotion.

Authors:  Juan C Moreno; Filipe Barroso; Dario Farina; Leonardo Gizzi; Cristina Santos; Marco Molinari; José L Pons
Journal:  J Neuroeng Rehabil       Date:  2013-07-19       Impact factor: 4.262

10.  Effects on mobility training and de-adaptations in subjects with Spinal Cord Injury due to a Wearable Robot: a preliminary report.

Authors:  Patrizio Sale; Emanuele Francesco Russo; Michele Russo; Stefano Masiero; Francesco Piccione; Rocco Salvatore Calabrò; Serena Filoni
Journal:  BMC Neurol       Date:  2016-01-28       Impact factor: 2.474

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