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All Journal Jurnal Otomasi Kontrol dan Instrumentasi
Risangtuni, Ayu Gareta
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Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Engineering Industrial & Manufacturing Engineering

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Klasifikasi Gerakan Pergelangan Tangan Berbasis Sinyal Elektromiografi dengan Fitur Root Mean Square dan Pengklasifikasi Support Vector Machine Rakhmadi, Imam; Risangtuni, Ayu Gareta; Suprijanto, Suprijanto
Jurnal Otomasi Kontrol dan Instrumentasi Vol 16 No 2 (2024): Jurnal Otomasi Kontrol dan Instrumentasi
Publisher : Pusat Teknologi Instrumentasi dan Otomasi (PTIO) - Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/joki.2024.16.2.10

Abstract

The wrist is essential to various human activities, with basic flexion, extension, and straight (normal) movements. These movements can be identified using surface electromyography (sEMG) signals that allow human interaction with computers through muscle activity, referred to as a muscle-computer interface. This research aims to perform the classification of flexion, extension, and normal movements as an initial stage of developing a muscle-computer interface system. The main stages of this research include data acquisition, signal processing, and motion classification with a support vector machine (SVM) offline and not in real-time, using root mean square (RMS) features with a moving window. This study successfully designed an effective sEMG signal measurement and processing method for motion classification. The designed classification algorithm showed high performance with an accuracy of 90.27%, precision of 90.61%, sensitivity of 90.17%, and f1-score of 90.27%, demonstrating the ability of the muscle-computer interface to classify wrist movements with a high degree of accuracy.
Co-Authors Rakhmadi, Imam Suprijanto Suprijanto