dc.contributor.authorCaesarendra, Wahyu
dc.contributor.authorTjahjowidodo, Tegoeh
dc.contributor.authorNico, Yohanes
dc.contributor.authorWahyudati, S.
dc.contributor.authorNurhasanah, L.
dc.date.accessioned2018-08-20T06:45:41Z
dc.date.available2018-08-20T06:45:41Z
dc.date.issued2018
dc.identifier.citationCaesarendra, W., Tjahjowidodo, T., Nico, Y., Wahyudati, S., & Nurhasanah, L. (2018). EMG finger movement classification based on ANFIS. Journal of Physics: Conference Series, 1007, 012005-. doi:10.1088/1742-6596/1007/1/012005en_US
dc.identifier.urihttp://hdl.handle.net/10220/45630
dc.description.abstractAn increase number of people suffering from stroke has impact to the rapid development of finger hand exoskeleton to enable an automatic physical therapy. Prior to the development of finger exoskeleton, a research topic yet important i.e. machine learning of finger gestures classification is conducted. This paper presents a study on EMG signal classification of 5 finger gestures as a preliminary study toward the finger exoskeleton design and development in Indonesia. The EMG signals of 5 finger gestures were acquired using Myo EMG sensor. The EMG signal features were extracted and reduced using PCA. The ANFIS based learning is used to classify reduced features of 5 finger gestures. The result shows that the classification of finger gestures is less than the classification of 7 hand gestures.en_US
dc.format.extent7 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesJournal of Physics: Conference Series*
dc.rights© 2018 The Author(s) (IOP Publishing). Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.en_US
dc.subjectEMG Signalen_US
dc.subjectFinger Movementen_US
dc.subjectDRNTU::Engineering::Mechanical engineeringen_US
dc.titleEMG finger movement classification based on ANFISen_US
dc.typeConference Paper
dc.contributor.conferenceInternational Conference on Mechanical, Electronics, Computer, and Industrial Technology (MECnIT 2018)en_US
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1088/1742-6596/1007/1/012005
dc.description.versionPublished versionen_US


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