{"title":"Low-cost photoplethysmograph solutions using the Raspberry Pi","authors":"T. Nagy, Z. Gingl","doi":"10.1109/CINTI.2013.6705184","DOIUrl":null,"url":null,"abstract":"Photoplethysmography is a prevalent, noninvasive heart monitoring method. In this paper an implementation of photoplethysmography on the Raspberry Pi is presented. Two modulation techniques are discussed, which make possible to measure these signals by the Raspberry Pi, using an external sound card as A/D converter. Furthermore, it is shown, how can digital signal processing improve signal quality. The presented methods can be used in low-cost cardiac function monitoring, in telemedicine applications and in education as well, since cheap and current hardware are used. Full documentation and open-source software for the measurement available: http://www.noise.inf.u-szeged.hu/Instruments/raspberryplet/.","PeriodicalId":439949,"journal":{"name":"2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI)","volume":"115 Suppl 2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CINTI.2013.6705184","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
Photoplethysmography is a prevalent, noninvasive heart monitoring method. In this paper an implementation of photoplethysmography on the Raspberry Pi is presented. Two modulation techniques are discussed, which make possible to measure these signals by the Raspberry Pi, using an external sound card as A/D converter. Furthermore, it is shown, how can digital signal processing improve signal quality. The presented methods can be used in low-cost cardiac function monitoring, in telemedicine applications and in education as well, since cheap and current hardware are used. Full documentation and open-source software for the measurement available: http://www.noise.inf.u-szeged.hu/Instruments/raspberryplet/.