Literature DB >> 31623545

Training for Walking Efficiency With a Wearable Hip-Assist Robot in Patients With Stroke: A Pilot Randomized Controlled Trial.

Hwang-Jae Lee1,2, Su-Hyun Lee1, Keehong Seo3, Minhyung Lee3, Won Hyuk Chang1, Byung-Ok Choi4, Gyu-Ha Ryu5, Yun-Hee Kim1,6.   

Abstract

Background and Purpose- The purpose of this study was to investigate the effects of gait training with a newly developed wearable hip-assist robot on locomotor function and efficiency in patients with chronic stroke. Methods- Twenty-eight patients with stroke with hemiparesis were initially enrolled, and 26 patients completed the randomized controlled trial (14 in the experimental and 12 in the control groups). The experimental group participated in a gait training program over a total of 10 sessions, including 5 treadmill sessions and 5 over-ground gait training sessions while wearing a hip-assist robot, the Gait Enhancing and Motivating System (GEMS, Samsung Advanced Institute of Technology, Suwon, Republic of Korea). The control group received gait training without Gait Enhancing and Motivating System. Primary outcome measured locomotor function and cardiopulmonary metabolic energy efficiency. Also, secondary outcome measured motor function and balance parameter. Results- Compared with the control group, the experimental group had significantly greater improvement in spatiotemporal gait parameters and muscle efforts after the training intervention (P<0.05). The net cardiopulmonary metabolic energy cost (mL·kg-1·min-1) was also reduced by 14.71% in the experimental group after the intervention (P<0.01). Significant group×time interactions were observed for all parameters (P<0.05). Cardiopulmonary metabolic efficiency was strongly correlated with gait symmetry ratio in the experimental group (P<0.01). Conclusions- Gait training with Gait Enhancing and Motivating System was effective for improving locomotor function and cardiopulmonary metabolic energy efficiency during walking in patients with stroke. These findings suggest that robotic locomotor training can be adopted for rehabilitation of patients with stroke with gait disorders. Clinical Trial Registration- URL: https://clinicaltrials.gov. Unique identifier: NCT02843828.

Entities:  

Keywords:  gait; locomotion; robotics; stroke rehabilitation; walking

Year:  2019        PMID: 31623545     DOI: 10.1161/STROKEAHA.119.025950

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


  16 in total

1.  Optimizing Exoskeleton Assistance for Faster Self-Selected Walking.

Authors:  Seungmoon Song; Steven H Collins
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2021-05-03       Impact factor: 3.802

2.  Effects of Bilateral Assistance for Hemiparetic Gait Post-Stroke Using a Powered Hip Exoskeleton.

Authors:  Yi-Tsen Pan; Inseung Kang; James Joh; Patrick Kim; Kinsey R Herrin; Trisha M Kesar; Gregory S Sawicki; Aaron J Young
Journal:  Ann Biomed Eng       Date:  2022-08-13       Impact factor: 4.219

3.  Foot contact forces can be used to personalize a wearable robot during human walking.

Authors:  Michael Jacobson; Prakyath Kantharaju; Hyeongkeun Jeong; Jae-Kwan Ryu; Jung-Jae Park; Hyun-Joon Chung; Myunghee Kim
Journal:  Sci Rep       Date:  2022-06-29       Impact factor: 4.996

4.  Activity-based training with the Myosuit: a safety and feasibility study across diverse gait disorders.

Authors:  Florian Leander Haufe; Kai Schmidt; Jaime Enrique Duarte; Peter Wolf; Robert Riener; Michele Xiloyannis
Journal:  J Neuroeng Rehabil       Date:  2020-10-08       Impact factor: 4.262

5.  Gait Recovery with an Overground Powered Exoskeleton: A Randomized Controlled Trial on Subacute Stroke Subjects.

Authors:  Franco Molteni; Eleonora Guanziroli; Michela Goffredo; Rocco Salvatore Calabrò; Sanaz Pournajaf; Marina Gaffuri; Giulio Gasperini; Serena Filoni; Silvano Baratta; Daniele Galafate; Domenica Le Pera; Placido Bramanti; Marco Franceschini
Journal:  Brain Sci       Date:  2021-01-14

6.  Benchmarking Wearable Robots: Challenges and Recommendations From Functional, User Experience, and Methodological Perspectives.

Authors:  Diego Torricelli; Carlos Rodriguez-Guerrero; Jan F Veneman; Simona Crea; Kristin Briem; Bigna Lenggenhager; Philipp Beckerle
Journal:  Front Robot AI       Date:  2020-11-13

7.  Robot-mediated overground gait training for transfemoral amputees with a powered bilateral hip orthosis: a pilot study.

Authors:  Clara Beatriz Sanz-Morère; Elena Martini; Simona Crea; Raffaele Molino-Lova; Nicola Vitiello; Barbara Meoni; Gabriele Arnetoli; Antonella Giffone; Stefano Doronzio; Chiara Fanciullacci; Andrea Parri; Roberto Conti; Francesco Giovacchini; Þór Friðriksson; Duane Romo
Journal:  J Neuroeng Rehabil       Date:  2021-07-03       Impact factor: 4.262

8.  Electromechanical-assisted training for walking after stroke.

Authors:  Jan Mehrholz; Simone Thomas; Joachim Kugler; Marcus Pohl; Bernhard Elsner
Journal:  Cochrane Database Syst Rev       Date:  2020-10-22

9.  Barriers to sEMG Assessment During Overground Robot-Assisted Gait Training in Subacute Stroke Patients.

Authors:  Michela Goffredo; Francesco Infarinato; Sanaz Pournajaf; Paola Romano; Marco Ottaviani; Leonardo Pellicciari; Daniele Galafate; Debora Gabbani; Annalisa Gison; Marco Franceschini
Journal:  Front Neurol       Date:  2020-10-19       Impact factor: 4.003

10.  Wearable hip-assist robot modulates cortical activation during gait in stroke patients: a functional near-infrared spectroscopy study.

Authors:  Su-Hyun Lee; Hwang-Jae Lee; Youngbo Shim; Won Hyuk Chang; Byung-Ok Choi; Gyu-Ha Ryu; Yun-Hee Kim
Journal:  J Neuroeng Rehabil       Date:  2020-10-29       Impact factor: 4.262

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