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|Title:||EMG finger movement classification based on ANFIS||Authors:||Caesarendra, Wahyu
|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
|DOI:||http://dx.doi.org/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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