Hen-Wei Huang, Philip Rupp, Jack Chen, Abhijay Kemkar, Naitik Khandelwal, Ian Ballinger, Peter Chai, Giovanni Traverso
{"title":"Cost-Effective Solution of Remote Photoplethysmography Capable of Real-Time, Multi-Subject Monitoring with Social Distancing.","authors":"Hen-Wei Huang, Philip Rupp, Jack Chen, Abhijay Kemkar, Naitik Khandelwal, Ian Ballinger, Peter Chai, Giovanni Traverso","doi":"10.1109/sensors52175.2022.9967120","DOIUrl":null,"url":null,"abstract":"<p><p>Recent advances in remote-photoplethysmography (rPPG) have enabled the measurement of heart rate (HR), oxygen saturation (SpO<sub>2</sub>), and blood pressure (BP) in a fully contactless manner. These techniques are increasingly applied clinically given a desire to minimize exposure to individuals with infectious symptoms. However, accurate rPPG estimation often leads to heavy loading in computation that either limits its real-time capacity or results in a costly setup. Additionally, acquiring rPPG while maintaining protective distance would require high resolution cameras to ensure adequate pixels coverage for the region of interest, increasing computational burden. Here, we propose a cost-effective platform capable of the real-time, continuous, multi-subject monitoring while maintaining social distancing. The platform is composed of a centralized computing unit and multiple low-cost wireless cameras. We demonstrate that the central computing unit is able to simultaneously handle continuous rPPG monitoring of five subjects with social distancing without compromising the frame rate and rPPG accuracy.</p>","PeriodicalId":74503,"journal":{"name":"Proceedings of IEEE Sensors. IEEE International Conference on Sensors","volume":"2022 ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9788727/pdf/nihms-1859505.pdf","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of IEEE Sensors. IEEE International Conference on Sensors","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/sensors52175.2022.9967120","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
Recent advances in remote-photoplethysmography (rPPG) have enabled the measurement of heart rate (HR), oxygen saturation (SpO2), and blood pressure (BP) in a fully contactless manner. These techniques are increasingly applied clinically given a desire to minimize exposure to individuals with infectious symptoms. However, accurate rPPG estimation often leads to heavy loading in computation that either limits its real-time capacity or results in a costly setup. Additionally, acquiring rPPG while maintaining protective distance would require high resolution cameras to ensure adequate pixels coverage for the region of interest, increasing computational burden. Here, we propose a cost-effective platform capable of the real-time, continuous, multi-subject monitoring while maintaining social distancing. The platform is composed of a centralized computing unit and multiple low-cost wireless cameras. We demonstrate that the central computing unit is able to simultaneously handle continuous rPPG monitoring of five subjects with social distancing without compromising the frame rate and rPPG accuracy.