Wireless Body Area Networks: A Survey

IEEE Communications Surveys &amp Tutorials (Impact Factor: 6.49). 01/2014; DOI: 10.1109/SURV.2013.121313.00064

ABSTRACT 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.

  • [Show description] [Hide description]
    DESCRIPTION: The last decade has witnessed the convergence of three giant worlds: electronics, computer science and telecommunications. The next decade should follow this convergence in most of our activities with the generalization of sensor networks. In particular with the progress in medicine, people live longer and the aging of the population will push the development of wireless personal networks (WPAN) and wireless body area networks (WBAN), as more and more people will prefer not to go to the hospital, should it be for convenience or for cost reasons. The recent advances in wireless technology have helped the development of wireless body area networks (WBAN), where a set of communicating devices gathered in or around a human body can communicate in an ad-hoc way. These devices are connected to sensors which monitor movements and vital body parameters. The WBAN has also been considered for sports, military and entertainment and thus, they are not only dedicated to medical and healthcare applications. The techniques involved are the latest emerging ones developed for general ad-hoc networks with a special attention to energy and reliability concerns due to the human lives that are involved.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The advancement in electronics, wireless communications and integrated circuits has enabled the development of small low-power sensors and actuators that can be placed on, in or around the human body. A wireless body area network (WBAN) can be effectively used to deliver the sensory data to a central server, where it can be monitored, stored and analyzed. For more than a decade, cognitive radio (CR) technology has been widely adopted in wireless networks, as it utilizes the available spectra of licensed, as well as unlicensed bands. A cognitive radio body area network (CRBAN) is a CR-enabled WBAN. Unlike other wireless networks, CRBANs have specific requirements, such as being able to automatically sense their environments and to utilize unused, licensed spectra without interfering with licensed users, but existing protocols cannot fulfill them. In particular, the medium access control (MAC) layer plays a key role in cognitive radio functions, such as channel sensing, resource allocation, spectrum mobility and spectrum sharing. To address various application-specific requirements in CRBANs, several MAC protocols have been proposed in the literature. In this paper, we survey MAC protocols for CRBANs. We then compare the different MAC protocols with one another and discuss challenging open issues in the relevant research.
    Sensors 04/2015; 15(4):9189-9209. DOI:10.3390/s150409189 · 2.05 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: An assessment of electrocardiogram (ECG) signal quality has become an unavoidable first step in most holter and ambulatory ECG signal analysis applications. In this paper, we present a simple method for automatically detection and classification of ECG noises. The proposed method consists of four major steps: moving average filter, blocking, feature extraction, and multistage decision-tree algorithm. In the proposed method, the dynamic amplitude range and autocorrelation maximum peak features are extracted for each block. In the first decision stage, a amplitude-dependent decision rule is used for detecting the presence of low-frequency (LF) noise (including, baseline wander (BW) and abrupt change (ABC) artifacts) and the high-frequency (HF) noise (including, power line interference (PLI) and muscle artifacts). In the second decision stage, the proposed method further classifies the LF noise into a BW noise or a ABC noise using the local dynamic amplitude range feature. The HF noise is classified into a PLI noise or a muscle noise using the local autocorrelation maximum peak feature. The proposed detection and classification method is tested and validated using a wide variety of clean and noisy ECG signals. Results show that the method can achieve an average sensitivity (Se) of 97.88%, positive productivity (+P) of 91.18% and accuracy of 89.06%.
    SPIN 2015; 02/2015


Available from
May 19, 2014