Wireless Body Area Networks: A Survey

IEEE Communications Surveys &amp Tutorials (Impact Factor: 6.81). 01/2014; DOI: 10.1109/SURV.2013.121313.00064


Recent developments and technological advancements in wireless communication, MicroElectroMechanical Systems (MEMS) technology and integrated circuits has enabled lowpower,
intelligent, miniaturized, invasive/non-invasive micro and nano-technology sensor nodes strategically placed in or around the human body to be used in various applications such as personal health monitoring. This exciting new area of research is called Wireless Body Area Networks (WBANs) and leverages the emerging IEEE 802.15.6 and IEEE 802.15.4j standards, specifically standardized for medical WBANs. The aim of WBANs is to simplify and improve speed, accuracy, and reliability of communications. The vast scope of challenges associated with WBANs has led to numerous publications. In this paper, we
survey the current state-of-art of WBANs based on the latest standards and publications. Open issues and challenges within each area are also explored as a source of inspiration towards future developments in WBANs.

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Available from: Samaneh Movassaghi, Mar 06, 2014
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    • "La Tabla II, resume las restricciones SAR básicas para todo el cuerpo y localizada entre 10 [MHz] y 10 [GHz]. Todos los TABLA II: RESUMEN DE RESTRICCIONES SAR [1] valores SAR se promedian sobre un período de 6 [min] con el fin de alcanzar un estado de equilibrio de temperatura [1]. En los tejidos, la SAR es proporcional al cuadrado de la intensidad del campo eléctrico en el interior. "
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    ABSTRACT: This paper gives an alternative method to determine the Specific Absorption Rate (SAR), amount of electromagnetic energy absorbed by the human body, using the numerical finite difference method with MATLAB software, from the electric fields generated by a type patch antenna applied to a Wireless Body Sensor Network (WBAN). These networks consist of bodily wireless sensors distributed inside or outside the human body for measuring physiological parameters. The research begins by defining electrical characteristics within the human body, to finally present and calculate the specific absorption rate SAR.
    IEEE Congreso Chileno de Ingeniería Eléctrica, Electrónica, Tecnologías de la Información y Comunicaciones IEEE CHILECON 2015. Santiago 10/2015, www.ucentral.cl/chilecon2015; 10/2015
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    • "BMSs are implantable (in-body), wearable (on-body), and/or installed away from the patient's body (offbody ) to monitor various vital signs in a patient body such as EEG, ECG, EMG, heartbeat, respiratory rate, temperature, blood pressure, glucose level, mental status, RUN, and WALK [3] [4] [5]. These types of BMSs are connected wirelessly with a centralized device, that is, body area network coordinator (BANC) [3] or body area network (BAN) [6] [7] [8] [9] as shown in Figure 1. Usually, patient data is classified into four classes, namely, critical data packet (CP), reliability data packet (RP), delay data packet (DP), and ordinary data packet (OP) [10]. "
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    ABSTRACT: Wireless body area network (WBAN) has brought revolutionary changes in the health domain for patients. Various biomedical sensors (BMSs) have been deployed to monitor various vital signs of a patient for detecting the abnormality of the vital signs and inform the medical staff in advance before the patient " s life goes into a threatening situation. In WBAN, routing layer has the same challenges as generally seen in WSN, but the unique requirements of WBANs need to be addressed by the novel routing mechanisms quite differently from the routing mechanism in wireless sensor networks (WSNs). The slots allocation to emergency and non-emergency patient " s data are the challenging issues in IEEE 802.15.4 and IEEE 802.15.6 MAC Superframe structures. In the similar way, IEEE 802.15.4 and IEEE 802.15.6 PHY layers have also unique constraints to modulate the various vital signs of patient data into continuous and discrete forms. Numerous research contributions have been made for addressing these issues of the aforementioned three layers in WBAN. Therefore, this paper presents a cross-layer design structure of WBAN with various issues and challenges. Moreover, it also presents a detail review of the existing cross-layer protocols in the WBAN domain by discussing their strengths and weaknesses. This study draws an attention towards the general understanding of routing, MAC and PHY layers in WBAN.
    Journal of Computer Networks and Communications 10/2015; DOI:10.1155/2015/516838
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    • "T HE introduction of lightweight and low-cost sensors has increased the potential for real time measurements of different activities. The advancements in microelectronics, wireless communications and other scientific areas has introduced the possibility of placing tiny sensor nodes on specific places of the body in order to monitor the health of patients or human body activities in general [1]. These sensors generate large amounts of data that need to be processed often in real-time. "
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    ABSTRACT: Collecting data at regular time nowadays is ubiquitous. The most widely used type of data that is being collected and analyzed is financial data and sensor readings. Various businesses have realized that financial time series analysis is a powerful analytical tool that can lead to competitive advantages. Likewise, sensor networks generate time series and if they are properly analyzed can give a better understanding of the processes that are being monitored. In this paper we propose a novel generic histogram-based method for feature engineering of time series data. The preprocessing phase consists of several steps: desean-sonalyzing the time series data, modeling the speed of change with first derivatives, and finally calculating histograms. By doing all of those steps the goal is threefold: achieve invariance to different factors, good modeling of the data and preform significant feature reduction. This method was applied to the AAIA Data Mining Competition 2015, which was concerned with recognition of activities carried out by firefighters by analyzing body sensor network readings. By doing that we were able to score the third place with predictive accuracy of about 83%, which was about 1% worse than the winning solution.
    Federated Conference On Computer Science And Information Systems, Lodz, Poland; 09/2015
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