A Wearable Gesture Recognition Device for Detecting Muscular Activities Based on Air-Pressure Sensors

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dc.contributor.authorJung, Pyeong-Gookko
dc.contributor.authorLim, Gukchanko
dc.contributor.authorKim, Seonghyokko
dc.contributor.authorKong, Kyoungchulko
dc.date.accessioned2019-01-23T06:20:24Z-
dc.date.available2019-01-23T06:20:24Z-
dc.date.created2019-01-11-
dc.date.created2019-01-11-
dc.date.issued2015-04-
dc.identifier.citationIEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, v.11, no.2, pp.485 - 494-
dc.identifier.issn1551-3203-
dc.identifier.urihttp://hdl.handle.net/10203/250045-
dc.description.abstractRecognition of human gestures plays an important role in a number of human-interactive applications, such as mobile phones, health monitoring systems, and human-assistive robots. Electromyography (EMG) is one of themost common and intuitive methods used for detecting gestures based on muscle activities. The EMG, however, is in general, too sensitive to environmental disturbances, such as electrical noise, electromagnetic signals, humidity, and so on. In this paper, a new method for recognizing the muscular activities is proposed based on air-pressure sensors and air-bladders. The muscular activity is detected by measuring the change of the air pressure in an air-bladder contacting the interested muscle(s). Since the change of the air pressure can be more robustly measured compared with the change of electric signals appeared on the skin, the proposed sensing method is useful for mobile devices due to its great signal-to-noise ratio (SNR) and fast response time. The principle and applications of the proposed sensing method are introduced in this paper. The performance of the proposed method is evaluated in terms of linearity, repeatability, wear-comfort, etc., and is also verified by comparing it with an EMG signal and a motion sensor.-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleA Wearable Gesture Recognition Device for Detecting Muscular Activities Based on Air-Pressure Sensors-
dc.typeArticle-
dc.identifier.wosid000352290400019-
dc.identifier.scopusid2-s2.0-84926433003-
dc.type.rimsART-
dc.citation.volume11-
dc.citation.issue2-
dc.citation.beginningpage485-
dc.citation.endingpage494-
dc.citation.publicationnameIEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS-
dc.identifier.doi10.1109/TII.2015.2405413-
dc.contributor.localauthorKong, Kyoungchul-
dc.contributor.nonIdAuthorJung, Pyeong-Gook-
dc.contributor.nonIdAuthorLim, Gukchan-
dc.contributor.nonIdAuthorKim, Seonghyok-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorElectromyography (EMG)-
dc.subject.keywordAuthorgesture recognition-
dc.subject.keywordAuthormechanomyography (MMG)-
dc.subject.keywordAuthormobile phones-
dc.subject.keywordAuthorwearable device-
dc.subject.keywordAuthorwearable sensors-
dc.subject.keywordPlusCONTRACTION-
dc.subject.keywordPlusMICROPHONE-
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