Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Journal of Engineering and Technological Sciences
Muchamad Arif Hana Sasono
Dept. of Electrical Engineering, Faculty of Engineering, University of Jember, Kalimantan Street No. 37, Sumbersari, Krajan Timur, Sumbersari, Sumbersari District, Jember Regency, East Java 68121, Indonesia
Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Feature Extraction Evaluation of Various Machine Learning Methods for Finger Movement Classification using Double Myo Armband Khairul Anam; Harun Ismail; Faruq Sandi Hanggara; Cries Avian; Safri Nahela; Muchamad Arif Hana Sasono
Journal of Engineering and Technological Sciences Vol. 55 No. 5 (2023)
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2023.55.5.8

Abstract

The deployment of electromyography (EMG) signals attracts many researchers since it can be used in decoding finger movements for exoskeleton robotics, prosthetics hand, and powered wheelchair. However, decoding any movement is a challenging task. The success of EMG signals' use lies in the appropriate choice of feature extraction and classification model, especially in the feature extraction process. Therefore, this study evaluates an eight-feature extraction evaluation on various machine learnings such as the Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree (DT), Naïve Bayes (NB), and Quadratic Discriminant Analysis (QDA). The dataset from four intact subjects is used to classify twelve finger movements. Through 5 cross-validations, the result shows that almost all feature extractions combined with SVM outperform other combinations of features and classifiers. Mean Absolute Value (MAV) as a feature and SVM as a classifier highlight the best combination with an accuracy of 94.01%.
Co-Authors Anam, Khairul Cries Avian Faruq Sandi Hanggara Harun Ismail Safri Nahela