Giannis Botilias, Lamprini Pappa, P. Karvelis, C. Stylios
{"title":"Tracking individuals’ health using mobile applications and Machine Learning","authors":"Giannis Botilias, Lamprini Pappa, P. Karvelis, C. Stylios","doi":"10.1109/SEEDA-CECNSM57760.2022.9932927","DOIUrl":null,"url":null,"abstract":"The widespread availability of smartphones and their high processing power have made them powerful mobile tools able to host and run various apps. In addition, wearable devices with low cost and accurate sensors gathering various physiological data and information are now available. Meanwhile, automated activity recognition is a rapidly evolving research area directly related to the mobile Health (mHealth) field. Rapid advancements in the Human Activity Recognition (HAR) field are mainly based on combining smartphones and wearable devices to succeed in advancing health tracking. This paper presents a mobile app designed and developed for monitoring changes in variables related to the physiological health status of an individual when he is moving around. The app tracks the physiological status of a human along with machine learning algorithms able to recognize and identify human activity and produce automatic alerts warning of dangerous health situations.","PeriodicalId":68279,"journal":{"name":"计算机工程与设计","volume":"1936 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"计算机工程与设计","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1109/SEEDA-CECNSM57760.2022.9932927","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The widespread availability of smartphones and their high processing power have made them powerful mobile tools able to host and run various apps. In addition, wearable devices with low cost and accurate sensors gathering various physiological data and information are now available. Meanwhile, automated activity recognition is a rapidly evolving research area directly related to the mobile Health (mHealth) field. Rapid advancements in the Human Activity Recognition (HAR) field are mainly based on combining smartphones and wearable devices to succeed in advancing health tracking. This paper presents a mobile app designed and developed for monitoring changes in variables related to the physiological health status of an individual when he is moving around. The app tracks the physiological status of a human along with machine learning algorithms able to recognize and identify human activity and produce automatic alerts warning of dangerous health situations.
期刊介绍：
Computer Engineering and Design is supervised by China Aerospace Science and Industry Corporation and sponsored by the 706th Institute of the Second Academy of China Aerospace Science and Industry Corporation. It was founded in 1980. The purpose of the journal is to disseminate new technologies and promote academic exchanges. Since its inception, it has adhered to the principle of combining depth and breadth, theory and application, and focused on reporting cutting-edge and hot computer technologies. The journal accepts academic papers with innovative and independent academic insights, including papers on fund projects, award-winning research papers, outstanding papers at academic conferences, doctoral and master's theses, etc.
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