A mobile wireless body area network platform |
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Institution: | 1. A. K. Choudhury School of Information Technology, University of Calcutta, Kolkata, India;2. Department of MCA, Future Institute of Engineering & Management, Kolkata, India;3. Department of Computer Applications, National Institute of Technology, Durgapur, India;4. Department of Computer Science & Engineering, University of Calcutta, Kolkata, India;5. Department of Computer Science, Winona State University, MN, USA;1. Université de Reims Champagne-Ardenne, CReSTIC, 7 Bld. Jean Delautre, 08000 Charleville-Mézières, France;2. Université de Franche-Comté, FEMTO-ST Institute, UMR CNRS 6174, 1 Cours Leprince-Ringuet, 25200 Montbéliard, France;3. Orange Labs 4 rue du Clos Courtel 35512 Cesson Sevigné Cedex, France |
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Abstract: | This paper aims to propose a system architecture for a mobile health-monitoring platform based on a wireless body area network (WBAN). We detail the WBAN features from either hardware and software point of view. The system architecture of this platform is three-tier system. Each tier is detailed. We have designed a flowchart of a use of the WBANs to illustrate the functioning of such platforms. We show the use of this platform in a wide area to detect and to track disease movement in the case of epidemic situation. Indeed, tracking epidemic disease is a very challenging issue. The success of such process could help medical administration to stop diseases quicker than usual. In this study, WBANs deployed over volunteers who agree to carry a light wireless sensor network. Sensors over the body will monitor some health parameters (temperature, pressure, etc) and will run some light classification algorithms to help disease diagnosis. Finally, the WBAN will send aggregated data about the disease to some base stations which collect the results. Our platform will run an on-line disease tracking program and to detect some information about how the disease is propagated. |
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Keywords: | Wireless sensor networks Wireless body area networks E-health Mobile networks Classification methodologies Disease diagnosis |
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