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      A Survey of Body Sensor Networks

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          Abstract

          The technology of sensor, pervasive computing, and intelligent information processing is widely used in Body Sensor Networks (BSNs), which are a branch of wireless sensor networks (WSNs). BSNs are playing an increasingly important role in the fields of medical treatment, social welfare and sports, and are changing the way humans use computers. Existing surveys have placed emphasis on the concept and architecture of BSNs, signal acquisition, context-aware sensing, and system technology, while this paper will focus on sensor, data fusion, and network communication. And we will introduce the research status of BSNs, the analysis of hotspots, and future development trends, the discussion of major challenges and technical problems facing currently. The typical research projects and practical application of BSNs are introduced as well. BSNs are progressing along the direction of multi-technology integration and intelligence. Although there are still many problems, the future of BSNs is fundamentally promising, profoundly changing the human-machine relationships and improving the quality of people's lives.

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          Activity identification using body-mounted sensors--a review of classification techniques.

          Robin H. Crompton, Alan Howard, John Y Goulermas … (2009)
          With the advent of miniaturized sensing technology, which can be body-worn, it is now possible to collect and store data on different aspects of human movement under the conditions of free living. This technology has the potential to be used in automated activity profiling systems which produce a continuous record of activity patterns over extended periods of time. Such activity profiling systems are dependent on classification algorithms which can effectively interpret body-worn sensor data and identify different activities. This article reviews the different techniques which have been used to classify normal activities and/or identify falls from body-worn sensor data. The review is structured according to the different analytical techniques and illustrates the variety of approaches which have previously been applied in this field. Although significant progress has been made in this important area, there is still significant scope for further work, particularly in the application of advanced classification techniques to problems involving many different activities.
          Article 0 comments Cited 128 times – based on 0 reviews     Review now
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            A comprehensive survey of Wireless Body Area Networks : on PHY, MAC, and Network layers solutions.

            Sana Ullah, Henry Higgins, Bart Braem … (2012)
            Recent advances in microelectronics and integrated circuits, system-on-chip design, wireless communication and intelligent low-power sensors have allowed the realization of a Wireless Body Area Network (WBAN). A WBAN is a collection of low-power, miniaturized, invasive/non-invasive lightweight wireless sensor nodes that monitor the human body functions and the surrounding environment. In addition, it supports a number of innovative and interesting applications such as ubiquitous healthcare, entertainment, interactive gaming, and military applications. In this paper, the fundamental mechanisms of WBAN including architecture and topology, wireless implant communication, low-power Medium Access Control (MAC) and routing protocols are reviewed. A comprehensive study of the proposed technologies for WBAN at Physical (PHY), MAC, and Network layers is presented and many useful solutions are discussed for each layer. Finally, numerous WBAN applications are highlighted.
            Article 0 comments Cited 115 times – based on 0 reviews     Review now
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              Artificial neural networks: opening the black box.

              Judith E. Dayhoff, James M. DeLeo, Steven Walczak (2001)
              Artificial neural networks now are used in many fields. They have become well established as viable, multipurpose, robust computational methodologies with solid theoretic support and with strong potential to be effective in any discipline, especially medicine. For example, neural networks can extract new medical information from raw data, build computer models that are useful for medical decision-making, and aid in the distribution of medical expertise. Because many important neural network applications currently are emerging, the authors have prepared this article to bring a clearer understanding of these biologically inspired computing paradigms to anyone interested in exploring their use in medicine. They discuss the historical development of neural networks and provide the basic operational mathematics for the popular multilayered perceptron. The authors also describe good training, validation, and testing techniques, and discuss measurements of performance and reliability, including the use of bootstrap methods to obtain confidence intervals. Because it is possible to predict outcomes for individual patients with a neural network, the authors discuss the paradigm shift that is taking place from previous "bin-model" approaches, in which patient outcome and management is assumed from the statistical groups in which the patient fits. The authors explain that with neural networks it is possible to mediate predictions for individual patients with prevalence and misclassification cost considerations using receiver operating characteristic methodology. The authors illustrate their findings with examples that include prostate carcinoma detection, coronary heart disease risk prediction, and medication dosing. The authors identify and discuss obstacles to success, including the need for expanded databases and the need to establish multidisciplinary teams. The authors believe that these obstacles can be overcome and that neural networks have a very important role in future medical decision support and the patient management systems employed in routine medical practice. Copyright 2001 American Cancer Society.
              Article 0 comments Cited 93 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Sensors (Basel)
                Journal ID (iso-abbrev): Sensors (Basel)
                Title: Sensors (Basel, Switzerland)
                Publisher: Molecular Diversity Preservation International (MDPI)
                ISSN (Electronic): 1424-8220
                Publication date Collection: May 2013
                Publication date (Electronic): 24 April 2013
                Volume: 13
                Issue: 5
                Pages: 5406-5447
                Affiliations
                [1 ] Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi District, Dalian 116024, China; E-Mails: weixin@ 123456mail.dlut.edu.cn (X.W.); wangwei@ 123456dlut.edu.cn (W.W.)
                [2 ] School of Software, Dalian University of Technology, Economy and Technology Development Area, Dalian 116620, China; E-Mails: zhou.guo.qiao@ 123456mail.dlut.edu.cn (G.Z.); hanguangyi@ 123456mail.dlut.edu.cn (G.H.)
                Author notes
                [* ] Authors to whom correspondence should be addressed; E-Mails: laixiaochen@ 123456dlut.edu.cn (X.L.); liuql@ 123456dlut.edu.cn (Q.L.); Tel.: +86-138-8941-5322 (X.L.); +86-133-3226-5366 (Q.L.); Fax: +86-411-8470-4005 (Q.L.).
                Article
                Publisher ID: sensors-13-05406
                DOI: 10.3390/s130505406
                PMC ID: 3690007
                PubMed ID: 23615581
                SO-VID: 17cdc293-d82e-4b19-b84e-19d83aeb2255
                Copyright © © 2013 by the authors; licensee MDPI, Basel, Switzerland.
                License:

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/

                History
                Date received : 19 December 2012
                Date revision received : 12 April 2013
                Date accepted : 19 April 2013
                Categories
                Subject: Review

                ScienceOpen disciplines: Biomedical engineering
                Keywords: body sensor network,sensor,data fusion,network communication,practical application of bsns
                Data availability:
                ScienceOpen disciplines: Biomedical engineering
                Keywords: body sensor network, sensor, data fusion, network communication, practical application of bsns

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