Literature DB >> 18002635

EMG-based neuro-fuzzy control of a 4DOF upper-limb power-assist exoskeleton.

Kazuo Kiguchi1, Yasunobu Imada, Manoj Liyanage.   

Abstract

We have been developing a 4DOF exoskeleton robot system in order to assist shoulder vertical motion, shoulder horizontal motion, elbow motion, and forearm motion of physically weak persons such as elderly, injured, or disabled persons. The robot is directly attached to a user's body and activated based on EMG (Electromyogram) signals of the user's muscles, since the EMG signals directly reflect the user's motion intention. A neuro-fuzzy controller has been applied to control the exoskeleton robot system. In this paper, controller adaptation method to user's EMG signals is proposed. A motion indicator is introduced to indicate the motion intention of the user for the controller adaptation. The experimental results show the effectiveness of the proposed method.

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Year:  2007        PMID: 18002635     DOI: 10.1109/IEMBS.2007.4352969

Source DB:  PubMed          Journal:  Annu Int Conf IEEE Eng Med Biol Soc        ISSN: 2375-7477


  3 in total

1.  Study of stability of time-domain features for electromyographic pattern recognition.

Authors:  Dennis Tkach; He Huang; Todd A Kuiken
Journal:  J Neuroeng Rehabil       Date:  2010-05-21       Impact factor: 4.262

2.  Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling.

Authors:  Guillaume Durandau; Dario Farina; Guillermo Asín-Prieto; Iris Dimbwadyo-Terrer; Sergio Lerma-Lara; Jose L Pons; Juan C Moreno; Massimo Sartori
Journal:  J Neuroeng Rehabil       Date:  2019-07-17       Impact factor: 4.262

3.  Latent Factors Limiting the Performance of sEMG-Interfaces.

Authors:  Sergey Lobov; Nadia Krilova; Innokentiy Kastalskiy; Victor Kazantsev; Valeri A Makarov
Journal:  Sensors (Basel)       Date:  2018-04-06       Impact factor: 3.576

  3 in total

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