Literature DB >> 28946579

A novel Robotic Gait Training System (RGTS) may facilitate functional recovery after stroke: A feasibility and safety study.

Li-Fong Lin1,2, Shih-Wei Huang1,3, Kwang-Hwa Chang4,5, Jin-Han Ouyang4, Tsan-Hon Liou1,3,5, Yen-Nung Lin4,5.   

Abstract

BACKGROUND: Robot-assisted gait training has been introduced as a practical treatment adjunctive to traditional stroke rehabilitation to provide high-intensity repetitive training. The design of robots is usually based on either the end-effector and exoskeleton method. The novel Robot Gait Training System (RGTS), a hybrid mixed type of end-effector and exoskeleton, tries to combine advantages from both methods.
OBJECTIVE: This preliminary study was conducted to report whether this novel system is feasible and safe when applied to non-ambulatory subacute patients with stroke.
METHODS: Six patients with stroke participated in this study and received 15 daily RGTS sessions. The outcome measures included the lower extremity subscale of the Fugl-Meyer Assessment (FMA-LE), Postural Assessment Scale for Stroke (PASS), Berg Balance Scale (BBS), and Barthel Index (BI). These measurements were performed at the pretest and posttest.
RESULTS: The RGTS demonstrated significant after-before changes in the FMA-LE, PASS, BBS and BI (p < 0.05), which indicated improvements substantially across the neurological status, balance, and activities of daily living after intervention.
CONCLUSIONS: This study demonstrated that the novel RGTS designed was practical, safe, and suitable to use in substantial leg dysfunction with stroke.

Entities:  

Keywords:  Robot gait training; balance; neuroplasticity; stroke

Mesh:

Year:  2017        PMID: 28946579     DOI: 10.3233/NRE-162137

Source DB:  PubMed          Journal:  NeuroRehabilitation        ISSN: 1053-8135            Impact factor:   2.138


  2 in total

1.  Hybrid robot-assisted gait training for motor function in subacute stroke: a single-blind randomized controlled trial.

Authors:  Yen-Nung Lin; Shih-Wei Huang; Yi-Chun Kuan; Hung-Chou Chen; Wen-Shan Jian; Li-Fong Lin
Journal:  J Neuroeng Rehabil       Date:  2022-09-14       Impact factor: 5.208

2.  Biomechanical Analysis in Five Bar Linkage Prototype Machine of Gait Training and Rehabilitation by IMU Sensor and Electromyography.

Authors:  Jeong-Woo Seo; Hyeong-Sic Kim
Journal:  Sensors (Basel)       Date:  2021-03-02       Impact factor: 3.576

  2 in total

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