Literature DB >> 33801552

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

Jeong-Woo Seo1, Hyeong-Sic Kim2.   

Abstract

The prototype machine of gait training and rehabilitation (MGTR) with a five-bar linkage structure was designed to improve the common end-effector type. Additionally, the study was conducted to evaluate the joint angle and muscle activity during walking for the evaluation of prototype: (1) Background: The gait rehabilitation systems are largely divided into exoskeletal type and end-effector type. The end-effector type can be improved a gait trajectory similar to normal gait according to this prototype. Therefore, a new design of prototype MGTR is proposed in this study. (2)
Methods: The gait experience was conducted with thirteen healthy male subjects using an inertial measurement unit (IMU) sensor and electromyography (EMG). It was compared that the hip and knee joints and the muscle activity between the normal gait and MGTR. (3)
Results: The results showed that there was a high correlation between the knee joint angle for normal gait and MGTR. The range of motion (RoM) was small for the MGTR. The EMG results showed that the activation of the rectus femoris muscle was most similar to the normal gait and MGTR. (4) Conclusions: The characteristics of the kinematic variables of the subjects varied widely. It is necessary to modify the machine so that the link length can be adjusted in consideration of various segment lengths of patients.

Entities:  

Keywords:  IMU sensor; electromyography; five-bar linkage; gait analysis; machine of gait training and rehabilitation

Mesh:

Year:  2021        PMID: 33801552      PMCID: PMC7958945          DOI: 10.3390/s21051726

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  19 in total

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2.  Reduced knee hyperextension after wearing a robotic knee orthosis during gait training--a case study.

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Journal:  Biomed Mater Eng       Date:  2015       Impact factor: 1.300

3.  Kinematic trajectories while walking within the Lokomat robotic gait-orthosis.

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Journal:  Clin Biomech (Bristol, Avon)       Date:  2008-10-11       Impact factor: 2.063

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Journal:  Med Biol Eng Comput       Date:  1998-03       Impact factor: 2.602

Review 5.  Plasticity during stroke recovery: from synapse to behaviour.

Authors:  Timothy H Murphy; Dale Corbett
Journal:  Nat Rev Neurosci       Date:  2009-11-04       Impact factor: 34.870

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

Authors:  Li-Fong Lin; Shih-Wei Huang; Kwang-Hwa Chang; Jin-Han Ouyang; Tsan-Hon Liou; Yen-Nung Lin
Journal:  NeuroRehabilitation       Date:  2017       Impact factor: 2.138

7.  Development of a body joint angle measurement system using IMU sensors.

Authors:  Saba Bakhshi; Mohammad H Mahoor; Bradley S Davidson
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2011

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Authors:  Stéphane Armand; Alice Bonnefoy-Mazure; Pierre Hoffmeyer; Geraldo De Coulon
Journal:  Rev Med Suisse       Date:  2015-10-14

9.  Robotic orthosis lokomat: a rehabilitation and research tool.

Authors:  Sašo Jezernik; Gery Colombo; Thierry Keller; Hansruedi Frueh; Manfred Morari
Journal:  Neuromodulation       Date:  2003-06-16

10.  Gait Event Detection for Stroke Patients during Robot-Assisted Gait Training.

Authors:  Andreas Schicketmueller; Juliane Lamprecht; Marc Hofmann; Michael Sailer; Georg Rose
Journal:  Sensors (Basel)       Date:  2020-06-16       Impact factor: 3.576

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