Literature DB >> 31197587

Controlling a Lower-Leg Exoskeleton Using Voltage and Current Variation Signals of a DC Motor Mounted at the Knee Joint.

Muhammad Al-Ayyad1, Bashar Al-Haj Moh'd2, Nidal Qasem3, Mohammad Al-Takrori4.   

Abstract

Powered exoskeleton technology helps turns dreams of recovering mobility after paralysis into reality. One of the most common problems encountered in the use of powered exoskeletons is the detection of the motion intentions of the user. Many approaches to conquering this problem have been developed using Electromyography (EMG) sensors, Electroencephalography (EEG) sensors, Center of Pressure (COP), and so forth. When a method, such as the surface EMG, is contaminated with noise during acquisition, it is important to process that raw EMG signal. Doing so usually takes time, and time delays in such a system can lead to a loss in synchronization between the wearer and the exoskeleton. Many algorithms have been developed for data acquisition and the filtering of raw EMG signals as well as accelerometer data. Our approach involves designing an almost sensor-less low limb exoskeleton that is powered by an electric Direct Current (DC) motor, and the same motor is used to detect motion via monitoring the voltage and the current variation. Experimental results are obtained for the actuating knee flexion-to-extension then extension-to-flexion of a sitting person using the National Instrument (NI) MyRIO as a data acquisition system with NI-LabView. The results support the hypothesis that the developed system can detect human motion and drive the motor in the necessary direction without the use of uncomfortable electrodes (sensors) and their connections. Additionally, the system supported the wearer to move his leg up (extension) without having too much effort to do so. In order to identify muscle activation with the change in the angle along the sagittal plane, an accelerometer has been attached to the system. The proposed approach could help open a new pathway along which researchers could develop low-cost and easy-to-wear powered exoskeletons which could emulate precisely the normal gait of a human.

Entities:  

Keywords:  Accelerometer; Current variation; DC motor; EMG; Exoskeleton; H-bridge; LabView; Lower limb; Sensor-less

Mesh:

Year:  2019        PMID: 31197587     DOI: 10.1007/s10916-019-1333-2

Source DB:  PubMed          Journal:  J Med Syst        ISSN: 0148-5598            Impact factor:   4.460


  8 in total

Review 1.  Exoskeletons for industrial application and their potential effects on physical work load.

Authors:  Michiel P de Looze; Tim Bosch; Frank Krause; Konrad S Stadler; Leonard W O'Sullivan
Journal:  Ergonomics       Date:  2015-10-07       Impact factor: 2.778

2.  Safety and tolerance of the ReWalk™ exoskeleton suit for ambulation by people with complete spinal cord injury: a pilot study.

Authors:  Gabi Zeilig; Harold Weingarden; Manuel Zwecker; Israel Dudkiewicz; Ayala Bloch; Alberto Esquenazi
Journal:  J Spinal Cord Med       Date:  2012-02-07       Impact factor: 1.985

Review 3.  Application of EMG signals for controlling exoskeleton robots.

Authors:  Christian Fleischer; Andreas Wege; Konstantin Kondak; Günter Hommel
Journal:  Biomed Tech (Berl)       Date:  2006-12       Impact factor: 1.411

Review 4.  Brain-machine interfaces for controlling lower-limb powered robotic systems.

Authors:  Yongtian He; David Eguren; José M Azorín; Robert G Grossman; Trieu Phat Luu; Jose L Contreras-Vidal
Journal:  J Neural Eng       Date:  2018-04       Impact factor: 5.379

5.  Concept proposal for a detachable exoskeleton-wheelchair to improve mobility and health.

Authors:  Jaimie F Borisoff; Johanne Mattie; Vince Rafer
Journal:  IEEE Int Conf Rehabil Robot       Date:  2013-06

6.  A simple exoskeleton that assists plantarflexion can reduce the metabolic cost of human walking.

Authors:  Philippe Malcolm; Wim Derave; Samuel Galle; Dirk De Clercq
Journal:  PLoS One       Date:  2013-02-13       Impact factor: 3.240

Review 7.  Surface electromyography signal processing and classification techniques.

Authors:  Rubana H Chowdhury; Mamun B I Reaz; Mohd Alauddin Bin Mohd Ali; Ashrif A A Bakar; K Chellappan; T G Chang
Journal:  Sensors (Basel)       Date:  2013-09-17       Impact factor: 3.576

8.  A Neural Network-Based Gait Phase Classification Method Using Sensors Equipped on Lower Limb Exoskeleton Robots.

Authors:  Jun-Young Jung; Wonho Heo; Hyundae Yang; Hyunsub Park
Journal:  Sensors (Basel)       Date:  2015-10-30       Impact factor: 3.576

  8 in total
  1 in total

Review 1.  Sensors and Actuation Technologies in Exoskeletons: A Review.

Authors:  Monica Tiboni; Alberto Borboni; Fabien Vérité; Chiara Bregoli; Cinzia Amici
Journal:  Sensors (Basel)       Date:  2022-01-24       Impact factor: 3.576

  1 in total

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