Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Journal of Applied Electrical Engineering
Risandriya, Sumantri K.
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : International Journal of Electrical and Computer Engineering

 1 
Enhancing sEMG finger gesture recognition using optimized 1D-convolutional neural network Pamungkas, Daniel Sutopo; Risandriya, Sumantri K.
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1576-1587

Abstract

Robust and precise finger gesture recognition using surface electromyography (sEMG) is essential for developing intuitive prosthetic control systems. However, sEMG signals are inherently stochastic and non-stationary, posing significant challenges for high-accuracy classification in fine-grained movements. This study proposes an optimized 1D convolutional neural network (1D-CNN) framework for classifying 20 distinct fine-grained finger gestures using raw sEMG data from an 8-channel wearable Myo Armband sensor. Unlike traditional methods that rely on manual feature engineering, the proposed 1D-CNN performs end-to-end learning to automatically extract temporal features. The research specifically investigates the impact of temporal windowing strategies, ranging from 400 to 750 ms, on model performance. Experimental results demonstrate that the optimized 1D-CNN achieves a peak test accuracy of 94.4% with a 550 ms window size, demonstrating the model’s robustness across complex gesture classes and significantly outperforming the baseline principal component analysis- support vector machine (PCA-SVM) method which only attained 73.0% accuracy. While the model achieved perfect classification (100%) for index, middle, and little finger movements, a performance drop was observed in thumb recognition (50%) due to muscular crosstalk from deeper anatomical layers. These findings indicate that the integration of optimized windowing and 1D-CNN architectures provides a highly reliable solution for complex large-scale gesture recognition, offering a robust foundation for the next generation of multi-functional prosthetic hands.
Co-Authors Asaad, Nur Sakinah Daniel Sutopo Pamungkas, Daniel Sutopo Muhammad Naufal Airlangga Diputra Nadhrah Wivanius, Nadhrah Octowinandi, Vivin Pratama, Adjie Sukma Ria, Riki