Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88131
Title: EMG finger movement classification based on ANFIS
Authors: Caesarendra, Wahyu
Tjahjowidodo, Tegoeh
Nico, Yohanes
Wahyudati, S.
Nurhasanah, L.
Keywords: EMG Signal
Finger Movement
DRNTU::Engineering::Mechanical engineering
Issue Date: 2018
Source: Caesarendra, 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:10.1088/1742-6596/1007/1/012005
Series/Report no.: Journal of Physics: Conference Series
Abstract: An 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.
URI: https://hdl.handle.net/10356/88131
http://hdl.handle.net/10220/45630
DOI: 10.1088/1742-6596/1007/1/012005
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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Conference Papers

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