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Muhammad Haristo Rahman
Jurusan Pendidikan Teknik Sipil Dan Perencanaan, Universitas Negeri Makassar.
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Public Health Social Sciences Transportation Other

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Deep Learning-Based Electromyography (EMG) Signal Classification for Robotic Hand Control Using Convolutional Neural Networks Mudarris, Mudarris; Rahman, Muhammad Haristo; Rahmah, Aulia; Munzir, Munzir
Jurnal Media Elektrik Vol. 23 No. 1 (2025): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i1.10264

Abstract

Electromyography (EMG) is one of the most essential bio signals for developing human–machine interfaces capable of translating muscle activity into motion commands, particularly in prosthetic and assistive robotic systems. However, the nonlinear characteristics of EMG, its susceptibility to noise, and its strong dependence on electrode placement make gesture classification a challenging task. This study aims to classify EMG signals for robotic hand control using a deep learning approach based on Convolutional Neural Networks (CNNs). The dataset consisted of 11,678 samples recorded from eight EMG channels across four hand gestures, preprocessed using a Butterworth filter and normalization prior to training with a lightweight CNN architecture. The model performance was evaluated using accuracy, precision, recall, and F1-score. The proposed model achieved an accuracy of 93%, outperforming Support Vector Machines (SVM), k-nearest neighbors (k-NN), and random forests under identical experimental conditions. The novelty of this study lies in the application of an efficient CNN architecture capable of extracting spatial–temporal features end-to-end from raw EMG signals for real-time robotic control. Despite its promising results, this study is limited to four gesture classes and is sensitive to electrode placement variability. These findings provide a foundational contribution to the development of more responsive, adaptive, and easily deployable prosthetic and robotic control systems.
Co-Authors Abdal, Nurul Mukhlisah Ahmad Rifqi Asrib Akmal Hidayat Akmal Hidayat Aminuddin Aminuddin, Amin Amiruddin Amiruddin Amiruddin Amiruddin Amiruddin Andi Ahmad Fauzan Bachtiar Andi Devina Yaritsha Darius Aprianti, Dwi Wahyuni Aqsha, Ismail Armiwaty Armiwaty Armiwaty Armiwaty Armiwaty, Armiwaty Asham Bin Jamaluddin Aulia Rahmah Della Fadhilatunisa Dewi Triantini Djuanda Djuanda, Djuanda Dwi Wahyuni Aprianti Fathahillah Fathahillah Fiskia Rera Baharuddin Furqan Ali Yusuf Hartini Ramli Hasim, Muhammad Hasrul, Muh. Reza Husnirrahman J Iriandy Iriandy, Iriandy Irika Widiasanti Irma Aswani Ahmad, Irma Aswani Irwansyah Suwahyu J, Husnirrahman Jusbaeni Labusab, Labusab M. Miftach Fakhri Mahjubuh Shiber, Nahridzah Mahmud, Amiruddin Marenda, Andi Moeh. Kay Muddin Asnur Mudarris Mudarris Mudarris, Mudarris Muh Bhilal Halim Muh. Aryanugraha Ismajaya Muh. Bhilal Halim Muhammad Alif Leo Muhammad Ansarullah Muhammad Asriadi Muhammad Hasim S Muhammad Romario Basirung Muhibuddin, Andi Firman Muhsin, Muhsin Z Munzir Munzir, Munzir Nahridzah Mahjubuh Shiber Ninik Rahayu Ashadi Noor Fadilah Romadhani Nurfadhilah, Ulfaizah Sahril NURLAELA NURLAELA Nurlita Pertiwi Nurlita pertiwi, Nurlita Pramono, Ashar Qadriathi Dg Bau Raeny Tenriola Idrus Rafika Hutami Putri Ramli, Hartini Ridwan Daud Mahande Rifqa Awalia Sahril Nurfadhilah, Ulfaizah Saragi, Alexander Adrian Setialaksana, Wirawan - SN, Ulfaizah Sudarmanto Jayanegara Sumariyanto, Iris Supardi, Rafsanjani Taufieq, Nur Anny S. Taufiq Natsir Tuti Iriani Umara Hasmarani Rizqiyah Wahyu Hidayat M Wahyu Saputra Wiranata, Tri Amartha