{"title":"PERS: Personalized environment recommendation system based on vital signs","authors":"A. Pravin Renold","doi":"10.1016/j.eij.2024.100580","DOIUrl":null,"url":null,"abstract":"<div><div>The integration of the Internet of Things (IoT) in healthcare has facilitated real-time monitoring of vital signs and environmental conditions. However, existing systems often lack personalized recommendations that consider the interplay between these factors. This work introduces the Personalized Environment Recommendation System (PERS), which leverages a portable device to continuously collect data on key health metrics, including pulse rate and body temperature, alongside environmental parameters. Utilizing Artificial Neural Networks, PERS analyzes the data to generate tailored health recommendations for users. Experimental results demonstrate an accuracy of 98.7%, highlighting the system’s effectiveness in enhancing patient care and supporting informed health decisions. The findings suggest that PERS can significantly improve health monitoring by providing actionable insights based on individual health profiles and environmental contexts.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100580"},"PeriodicalIF":5.0000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Egyptian Informatics Journal","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1110866524001439","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
The integration of the Internet of Things (IoT) in healthcare has facilitated real-time monitoring of vital signs and environmental conditions. However, existing systems often lack personalized recommendations that consider the interplay between these factors. This work introduces the Personalized Environment Recommendation System (PERS), which leverages a portable device to continuously collect data on key health metrics, including pulse rate and body temperature, alongside environmental parameters. Utilizing Artificial Neural Networks, PERS analyzes the data to generate tailored health recommendations for users. Experimental results demonstrate an accuracy of 98.7%, highlighting the system’s effectiveness in enhancing patient care and supporting informed health decisions. The findings suggest that PERS can significantly improve health monitoring by providing actionable insights based on individual health profiles and environmental contexts.
期刊介绍：
The Egyptian Informatics Journal is published by the Faculty of Computers and Artificial Intelligence, Cairo University. This Journal provides a forum for the state-of-the-art research and development in the fields of computing, including computer sciences, information technologies, information systems, operations research and decision support. Innovative and not-previously-published work in subjects covered by the Journal is encouraged to be submitted, whether from academic, research or commercial sources.
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