K. Saraubon, Keattisuk Anurugsa, Adichart Kongsakpaibul
{"title":"A Smart System for Elderly Care using IoT and Mobile Technologies","authors":"K. Saraubon, Keattisuk Anurugsa, Adichart Kongsakpaibul","doi":"10.1145/3301761.3301769","DOIUrl":null,"url":null,"abstract":"Population aging is becoming a pressing issue for society. The number of elderly people, those aged 60 years and over, is increasing dramatically in many countries. A great number of elderly people stay alone at home while young people in their family go out to work. This paper presents a smart system designed and developed for elderly care using IoT and mobile technologies. The features of the system include acoustic-based and accelerometer-based fall detection, real-time remote video monitoring on mobile devices, voice commands and heart rate monitoring. The evaluation matrix shows that the accuracy, precision and recall of the accelerometer-based approach were 93.3%, 92.6% and 94.3%, respectively, while the acoustic-based approach achieved 78.6%, 76.9% and 80.6%, respectively.","PeriodicalId":325887,"journal":{"name":"ICSEB '18","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICSEB '18","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3301761.3301769","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18
Abstract
Population aging is becoming a pressing issue for society. The number of elderly people, those aged 60 years and over, is increasing dramatically in many countries. A great number of elderly people stay alone at home while young people in their family go out to work. This paper presents a smart system designed and developed for elderly care using IoT and mobile technologies. The features of the system include acoustic-based and accelerometer-based fall detection, real-time remote video monitoring on mobile devices, voice commands and heart rate monitoring. The evaluation matrix shows that the accuracy, precision and recall of the accelerometer-based approach were 93.3%, 92.6% and 94.3%, respectively, while the acoustic-based approach achieved 78.6%, 76.9% and 80.6%, respectively.