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All Journal Knowledge Engineering and Data Science JECCOM
Hendrick Hendrick
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Segmentation Method for Face Modelling in Thermal Images Albar Albar; Hendrick Hendrick; Rahmad Hidayat
Knowledge Engineering and Data Science Vol 3, No 2 (2020)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v3i22020p99-105

Abstract

Face detection is mostly applied in RGB images. The object detection usually applied the Deep Learning method for model creation. One method face spoofing is by using a thermal camera. The famous object detection methods are Yolo, Fast RCNN, Faster RCNN, SSD, and Mask RCNN. We proposed a segmentation Mask RCNN method to create a face model from thermal images. This model was able to locate the face area in images. The dataset was established using 1600 images. The images were created from direct capturing and collecting from the online dataset. The Mask RCNN was configured to train with 5 epochs and 131 iterations. The final model predicted and located the face correctly using the test image.
Design of Basic Vital Signs Measurement Tool And Dehydration Early Detection in Human Body Efrizon Efrizon; Gwo Jia Jong; Hendrick Hendrick; fadhlan; Yulastri Yulastri
JECCOM: International Journal of Electronics Engineering and Applied Science Vol. 1 No. 1 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30642/jeccom.1.1.1-8.2023

Abstract

A tool has been made to monitor vital signs such as heart rate, oxygen saturation in the blood, and body temperature based on a microcontroller which is based on dehydration conditions where the body loses more fluid than the amount of fluid it enters. Parameters for carrying out this detection include heart rate, blood oxygen saturation (SpO2), body temperature and urine color. The targets of this research are (a) making a prototype, (b) programming the system with the help of the Arduino IDE, and (c) measuring system performance. The research method starts from making a prototype and measuring system performance. The results of measuring the performance of the tool show that the error for measuring heart rate is 1.28%, measuring blood oxygen saturation (SpO2) is 0.51%, and measuring body temperature is 1.729%. However, for the dehydration detection test from 5 test samples, the results showed a success percentage of 60% with an average error of 40%. Overall the tool can function well
Co-Authors Albar Albar fadhlan Gwo Jia Jong Rahmat Hidayat Yulastri Yulastri