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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Informatika Komputer MediaTor: Jurnal Komunikasi Jurnal KOMUNIKA TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmu Komunikasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Kajian Komunikasi Jurnal Ilmiah FIFO Jurnal ASPIKOM Khadimul Ummah Journal of Social Dedication Journal of Applied Data Sciences Komunika: Jurnal Dakwah dan Komunikasi Jurnal Pengabdian Kepada Masyarakat Darma Saskara
Hustinawaty Hustinawaty, Hustinawaty
Universitas Gunadarma
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

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Nonintrusive Arrhythmia Detection from Wrist Pulse Using NTSC Color Model in Eulerian Video Magnification Basyah, Baby Lolita; Hustinawaty, Hustinawaty; Jannah, Miftahul
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1293

Abstract

Arrhythmia is a cardiovascular condition characterized by abnormal heart rhythms, such as tachycardia and bradycardia, which may lead to serious health complications if not detected early. This study proposes a non-invasive approach for screening tachycardia by extracting pulse signals from wrist video recordings using Eulerian Video Magnification (EVM) combined with the NTSC color space model. Subtle variations in skin color caused by blood flow, which are typically imperceptible to the human eye, are amplified using the EVM technique to enhance pulse-related motion signals. The NTSC color model is employed to separate luminance and chrominance components (YIQ), allowing more effective identification of pulse-induced color variations in the wrist region. The recorded wrist videos are processed through several stages, including spatial decomposition, temporal filtering, motion magnification, and pixel intensity extraction from the region of interest to obtain a temporal pulse signal. Peak detection is then applied to estimate heart rate in beats per minute (BPM). The performance of the proposed method is evaluated by comparing the estimated BPM values with reference measurements obtained from a Xiaomi Mi Band 2 wearable device. Experimental results based on 20 wrist video recordings demonstrate that the proposed method achieves approximately 96% agreement between the estimated BPM values and the reference measurements. Quantitative evaluation using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and correlation analysis further confirms the consistency of the proposed approach. These results indicate that the integration of Eulerian Video Magnification with the NTSC color model has potential as a low-cost and non-contact method for preliminary tachycardia screening and remote cardiovascular monitoring.
Image feature extraction for road surface damage classification Hutapea, Octaviani; Madenda, Sarifuddin; Hustinawaty, Hustinawaty; Mardhiyah, Iffatul
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1578-1592

Abstract

Road surface deterioration poses a critical risk to driving safety and comfort, necessitating timely and accurate detection to support effective maintenance. Manual inspection methods are often inefficient, underscoring the need for automated approaches based on computer vision. This study investigates the integration of feature extraction techniques histogram of oriented gradients (HOG) and local binary pattern (LBP) with convolutional neural network (CNN) architectures ResNet50 and InceptionV3 for the classification of road damage. A dataset of 1,580 images was categorized into five damage types: alligator crack, longitudinal crack, other crack, patching, and potholes. Experimental results indicate that HOG–ResNet50 achieved 79% accuracy, while LBP–InceptionV3 yielded the best performance at 97%. The contributions of this study are threefold: i) an automated framework is proposed that combines texture-based features with deep learning for road damage detection, ii) the LBP–InceptionV3 combination is shown to provide superior accuracy compared to conventional pairings, and iii) the approach offers a scalable and reliable alternative to manual inspection methods, supporting more efficient road maintenance planning.
Co-Authors Abdullah Abdullah Abdullah Abdullah Achmad Benny Mutiara Ahmad Hidayat Arkan, Faisal Ary Bima Kurniawan Ary Bima Kurniawan Baby Lolita B., Baby Lolita Dewi Agushinta R. Dwiputra, Rama Al Azis Edy Prihantoro Eldianisa Adesita Eri Prasetyo Wibowo, Eri Prasetyo Faisal Arkan Henny Medyawati Hutapea, Octaviani Ihsan Jatnika Mardhiyah, Iffatul Matrissya Hermita Miftahul Jannah Notonegoro, Radityo Hendratmojo Jati Nur Sultan Salahuddin Purwoko, Agus Rifiana Arief Rila Setyaningsih Rila Setyaningsih Rizqi Basuki, Nurfadjri Akbar Sari, Sri Purnomo Sarifuddin Madenda Setyaningsih, Rila Suger, Herik Sulardi . Supiani Supiani, Supiani Suryarini Widodo Suryarini Widodo Tavipia Rumambi Tavipia Rumambi, Tavipia Tubagus Maulana Kusuma Veithzal Rivai Zainal