Mohammed F. Alhamid, M. Eid, Abdulrhman M Alshareef, Abdulmotaleb El Saddik
{"title":"MMBIP: Biofeedback system design on Cloud-Oriented Architecture","authors":"Mohammed F. Alhamid, M. Eid, Abdulrhman M Alshareef, Abdulmotaleb El Saddik","doi":"10.1109/ROSE.2012.6402610","DOIUrl":null,"url":null,"abstract":"in this paper, we propose a biofeedback system that employs a Cloud-Oriented Architecture (COA) for the dissemination of biofeedback information and services. The architecture provides the software infrastructure to build biofeedback applications that maintain the user's well-being by monitoring a number of physiological parameters and generate the appropriate feedback. Consequently, the architecture combines the collection of various sensory physiological data and utilizes the existing cloud of resources to provide processing, storage, and responses for biofeedback applications. The performance evaluation has shown three distinguished features of the proposed architecture, namely adaptability for various sensory streams, soft real-timeliness, and scalability.","PeriodicalId":306272,"journal":{"name":"2012 IEEE International Symposium on Robotic and Sensors Environments Proceedings","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Symposium on Robotic and Sensors Environments Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROSE.2012.6402610","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
in this paper, we propose a biofeedback system that employs a Cloud-Oriented Architecture (COA) for the dissemination of biofeedback information and services. The architecture provides the software infrastructure to build biofeedback applications that maintain the user's well-being by monitoring a number of physiological parameters and generate the appropriate feedback. Consequently, the architecture combines the collection of various sensory physiological data and utilizes the existing cloud of resources to provide processing, storage, and responses for biofeedback applications. The performance evaluation has shown three distinguished features of the proposed architecture, namely adaptability for various sensory streams, soft real-timeliness, and scalability.