Academic Press Library in Biomedical Applications of Mobile and Wireless Communications: Wireless UWB Body Area Networks Using the IEEE802.15.4-2011

Wireless sensor and body area networks (WSN and WBAN respectively) have been seen as a future way to monitor humans' psycho-physiological signs remotely. There are a number of standards that could be used for building WBAN sytems. However, wireless UWB networks based on IEEE 802.15.4a offer the advantages of a large frequency range and low power spectral density, making it suitable for both WSNs and WBANs used for medical applications. The technology has matured sufficiently that it can be used to develop products for the marketplace. This book presents how the IEEE802.15.4-2011 (former IEEE802.15.4a) can be used in wireless body area networks (WBAN) for healthcare and welfare related applications. It gives a short overview on the IEEE802.15.4 family and then gives details of IEEE802.15.4-2011 based solutions.

• Presents how the  IEEE802.15.4-2011 (former IEEE802.15.4a) can be used in  wireless body area networks (WBAN) for healthcare and welfare related applications. 
• Gives a short overview on the IEEE802.15.4 family.
• Gives details of IEEE802.15.4-2011 based solutions. 

Dr. Hämäläinen is Professor of Radiology at Massachusetts General hospital and Director of Magnetoencephalography (MEG) Core at Athinoula A. Martinos Center for Biomedical Imaging at MGH. He is one of the pioneers in the application of MEG in conjunction with other non-invasive functional and anatomical imaging methods to study human brain function. He has had a crucial role in developing whole-head MEG instrumentation, analytical methods and tools, as well as experimental protocols, which have together paved the way for MEG becoming an important basic research and clinical tool worldwide.
In 1993 he was the co-author of a seminal review article on MEG in Reviews of Modern Physics, now with more than 4000 citations. His current research interests include further development of anatomically-constrained MEG/EEG source estimation methods, including sparse and temporally continuous approaches, combination of non-invasive and invasive electromagnetic source imaging with hemodynamic measures, MEG/EEG studies of early brain development in infants, and adapting and extending MEG/EEG analysis methods to be applicable in real time in clinical neurophysiology studies.
Lorenzo Mucchi is an assistant professor at the Dept. of Information Engineering of the University of Florence, Italy, where he teaches Information Technologies. Lorenzo's main research areas include theoretical modelling, algorithms design and real measurements, mainly focused in the following fields: ultra wideband signals, localization and tracking, interference/channel modelling, intrinsic wireless security, adaptive diversity techniques and wireless healthcare. Lorenzo is senior member of the Institute of Electrical and Electronics Engineers (IEEE) and permanent member of the International Association of Science and Technology for Development (IASTED) Technical Committee on Telecommunications. All details are available at: http://lenst.det.unifi.it/~mucchi/