Forearm motion assist with an exoskeleton adaptation to muscle activation patterns

Kazuo Kiguchi; Ryo Esaki; Toshio Fukuda

Forearm motion assist with an exoskeleton adaptation to muscle activation patterns

Kazuo Kiguchi, Ryo Esaki, Toshio Fukuda

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper presents an exoskeleton for the assist of forearm motion (elbow flexion-extension and forearm pronation-supination motion) in daily activity and rehabilitation. The exoskeleton is controlled based on the activation patterns of the electromyogram (EMG) signals of the patient's muscles, which directly reflects the motion intention of the patient, in order to realize natural motion assist. The sophisticated real-time neuro-fuzzy control method, in which the effect of a muscle common to both motions is taken into account, is proposed. The proposed control method enables the cooperative motion of elbow and forearm of the patient by learning the muscle activation patterns of each patient. The effectiveness of the proposed method was evaluated by experiment.

Original language	English
Title of host publication	2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Pages	2948-2953
Number of pages	6
Publication status	Published - Dec 1 2004
Externally published	Yes
Event	2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) - Sendai, Japan Duration: Sep 28 2004 → Oct 2 2004

Publication series

Name	2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Volume	3

Other

Other	2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Country	Japan
City	Sendai
Period	9/28/04 → 10/2/04

All Science Journal Classification (ASJC) codes

Engineering(all)

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Cite this

Forearm motion assist with an exoskeleton adaptation to muscle activation patterns. / Kiguchi, Kazuo; Esaki, Ryo; Fukuda, Toshio.

2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2004. p. 2948-2953 (2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); Vol. 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kiguchi, K, Esaki, R & Fukuda, T 2004, Forearm motion assist with an exoskeleton adaptation to muscle activation patterns. in 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 3, pp. 2948-2953, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Sendai, Japan, 9/28/04.

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abstract = "This paper presents an exoskeleton for the assist of forearm motion (elbow flexion-extension and forearm pronation-supination motion) in daily activity and rehabilitation. The exoskeleton is controlled based on the activation patterns of the electromyogram (EMG) signals of the patient's muscles, which directly reflects the motion intention of the patient, in order to realize natural motion assist. The sophisticated real-time neuro-fuzzy control method, in which the effect of a muscle common to both motions is taken into account, is proposed. The proposed control method enables the cooperative motion of elbow and forearm of the patient by learning the muscle activation patterns of each patient. The effectiveness of the proposed method was evaluated by experiment.",

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AB - This paper presents an exoskeleton for the assist of forearm motion (elbow flexion-extension and forearm pronation-supination motion) in daily activity and rehabilitation. The exoskeleton is controlled based on the activation patterns of the electromyogram (EMG) signals of the patient's muscles, which directly reflects the motion intention of the patient, in order to realize natural motion assist. The sophisticated real-time neuro-fuzzy control method, in which the effect of a muscle common to both motions is taken into account, is proposed. The proposed control method enables the cooperative motion of elbow and forearm of the patient by learning the muscle activation patterns of each patient. The effectiveness of the proposed method was evaluated by experiment.

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