Sistem Monitoring dan Deteksi Stres Pada Anak Berbasis Wearable Device

The need for monitoring of children today is very important, especially for children under five, whose physical and verbal abilities are still inadequate to be able to communicate effectively with their parents or caregivers about the conditions they are experiencing. Previous studies related to chi...

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Autores principales:	Phie Chyan, Yudi Kasmara
Formato:	article
Lenguaje:	ID
Publicado:	Ikatan Ahli Indormatika Indonesia 2021
Materias:	child monitoring system, stress detection, wearable device, machine learning Systems engineering TA168 Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/4270b78f74b947c6afa789c7bc5c1ea1
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spelling	oai:doaj.org-article:4270b78f74b947c6afa789c7bc5c1ea12021-11-16T13:16:11ZSistem Monitoring dan Deteksi Stres Pada Anak Berbasis Wearable Device2580-076010.29207/resti.v5i5.3503https://doaj.org/article/4270b78f74b947c6afa789c7bc5c1ea12021-10-01T00:00:00Zhttp://jurnal.iaii.or.id/index.php/RESTI/article/view/3503https://doaj.org/toc/2580-0760The need for monitoring of children today is very important, especially for children under five, whose physical and verbal abilities are still inadequate to be able to communicate effectively with their parents or caregivers about the conditions they are experiencing. Previous studies related to child safety that were studied generally focused on responses to events that could potentially harm children.This study aims to design a prototype child monitoring system consisting of a wearable device that is used on a child's wrist equipped with sensors and connected to a server via a wireless network. Monitoring software that runs on the server will collect all data parameters from wearable devices with built-in audio signal, temperature, and heart rate sensor then with machine learning algorithm implemented in software will allow the system to predict if stress conditions happen on children and then system can give warnings to child-caregiver through monitoring applications or SMS messages. Using the Decision Tree and Naive Bayes classification methods the system can effectively predict stress conditions in children with an accuracy of 82.8 percent using audio, temperature, and heart rate parameters. This shows that the system has the capability to contribute to increasing child safety in the supervision environment.Phie ChyanYudi KasmaraIkatan Ahli Indormatika Indonesiaarticlechild monitoring system, stress detection, wearable device, machine learningSystems engineeringTA168Information technologyT58.5-58.64IDJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), Vol 5, Iss 5, Pp 943-949 (2021)
institution	DOAJ
collection	DOAJ
language	ID
topic	child monitoring system, stress detection, wearable device, machine learning Systems engineering TA168 Information technology T58.5-58.64
spellingShingle	child monitoring system, stress detection, wearable device, machine learning Systems engineering TA168 Information technology T58.5-58.64 Phie Chyan Yudi Kasmara Sistem Monitoring dan Deteksi Stres Pada Anak Berbasis Wearable Device
description	The need for monitoring of children today is very important, especially for children under five, whose physical and verbal abilities are still inadequate to be able to communicate effectively with their parents or caregivers about the conditions they are experiencing. Previous studies related to child safety that were studied generally focused on responses to events that could potentially harm children.This study aims to design a prototype child monitoring system consisting of a wearable device that is used on a child's wrist equipped with sensors and connected to a server via a wireless network. Monitoring software that runs on the server will collect all data parameters from wearable devices with built-in audio signal, temperature, and heart rate sensor then with machine learning algorithm implemented in software will allow the system to predict if stress conditions happen on children and then system can give warnings to child-caregiver through monitoring applications or SMS messages. Using the Decision Tree and Naive Bayes classification methods the system can effectively predict stress conditions in children with an accuracy of 82.8 percent using audio, temperature, and heart rate parameters. This shows that the system has the capability to contribute to increasing child safety in the supervision environment.
format	article
author	Phie Chyan Yudi Kasmara
author_facet	Phie Chyan Yudi Kasmara
author_sort	Phie Chyan
title	Sistem Monitoring dan Deteksi Stres Pada Anak Berbasis Wearable Device
title_short	Sistem Monitoring dan Deteksi Stres Pada Anak Berbasis Wearable Device
title_full	Sistem Monitoring dan Deteksi Stres Pada Anak Berbasis Wearable Device
title_fullStr	Sistem Monitoring dan Deteksi Stres Pada Anak Berbasis Wearable Device
title_full_unstemmed	Sistem Monitoring dan Deteksi Stres Pada Anak Berbasis Wearable Device
title_sort	sistem monitoring dan deteksi stres pada anak berbasis wearable device
publisher	Ikatan Ahli Indormatika Indonesia
publishDate	2021
url	https://doaj.org/article/4270b78f74b947c6afa789c7bc5c1ea1
work_keys_str_mv	AT phiechyan sistemmonitoringdandeteksistrespadaanakberbasiswearabledevice AT yudikasmara sistemmonitoringdandeteksistrespadaanakberbasiswearabledevice
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Sistem Monitoring dan Deteksi Stres Pada Anak Berbasis Wearable Device

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