Estimation of user's hand motion based on EMG and EEG signals

Kazuo Kiguchi, Kaori Tamura, Yoshiaki Hayashi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

A surface EMG signal is one of the most widely used signals as input signals to wearable robots. However, EMG signals that are used to estimate motions are not always available to all users. On the other hand, an EEG signal has drawn attention as input signals for those robots in recent years. The EEG signals can be measured even with amputees and paralyzed patients who are not able to generate some EMG signals. However, the measured EEG signal does not have one-to-one relationships with the corresponding brain part. Therefore, it is more difficult to find the required signals for the control of the robot in accordance with the intention of the user's motion using the EEG signals compared with that using the EMG signals. In this paper, both the EMG and EEG signals are used to estimate the user's motion intention. In the proposed method, the EMG signals are used as main input signals because the EMG signals have higher relative to the motion of a user in comparison with the EEG signals. The EEG signals are used as sub signals in order to cover the estimation of the intention of the user's motion when all required EMG signals cannot be measured. The effectiveness of the proposed method has been evaluated by performing experiments.

Original languageEnglish
Title of host publicationWorld Automation Congress Proceedings
PublisherIEEE Computer Society
Pages713-717
Number of pages5
ISBN (Electronic)9781889335490
DOIs
Publication statusPublished - Oct 24 2014
Event2014 World Automation Congress, WAC 2014 - Waikoloa, United States
Duration: Aug 3 2014Aug 7 2014

Publication series

NameWorld Automation Congress Proceedings
ISSN (Print)2154-4824
ISSN (Electronic)2154-4832

Other

Other2014 World Automation Congress, WAC 2014
CountryUnited States
CityWaikoloa
Period8/3/148/7/14

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

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