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Wireless Body Area Network

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The purpose of this presentation is to introduce the Body Area Network technology. At the beginning I have discussed the history and development of Body Sensor Network and how that grew into the more general concept of BAN. MobiHealth as a mature example of MBSN technology has been explained. I then continued on to take a look at a case study involving MobiHealth and the monitoring cardiac data. I concluded the paper by looking at some challenges related to BAN. We covered signal and path loss in the human body and some of the challenges associated with communication and power within the human body. This ppt demonstrates usability and the fusion of cutting edge technology and how it is shaping wearable technology.

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Wireless Body Area Network

  1. 1. Presented By Faheema Monica (莫妮卡) ID - s20141501 University of Science & Technology, Beijing
  2. 2. • 1. Introduction to Body Area Network (BAN) • 2. History and Development of BAN • 3. Architecture of BAN • 4. Applications in Healthcare • 5. Challenges associated with BAN • 6. Conclusion
  3. 3.  A body area network (BAN) is a network that includes a collection of wearable devices. It is a specific type of wireless network with a very particular use and scope  A Body Area Network is formally defined by IEEE 802.15 as, "a communication standard optimized for low power devices and operation on, in or around the human body (but not limited to humans) to serve a variety of applications including medical, consumer electronics / personal entertainment and other" [IEEE 802.15]
  4. 4. Sensors used in MBAN are classified by two main categories:  A wearable BAN -for physiological monitoring  An implantable BAN- diabetes management, drug delivery through a micro-pump or micro-port, insulin. Figure 1: MBAN sensors
  5. 5.  Professor Guang Zhong Yang was the first person to formally define the phrase "Body Sensor Network" (BSN) with publication of his book Body Sensor Networks in 2006.  Some of the common use cases for BAN technology are: • Body Sensor Networks (BSN) • Sports and Fitness Monitoring • Wireless Audio • Mobile Device Integration • Personal Video Devices Prof Guang Zhong Yang, Director, The Hamlyn Centre Imperial College London.UK.
  6. 6. 1000 mW500 mW100 mW50 mW10 mW 1 Gbit/s 100 kbit/s 1 Mbit/s 10 Mbit/s 100 Mbit/s 1 kbit/s 10 kbit/s Wireless USB IEEE 802.11 a/b/g Bluetooth ZigBee 200 mW20 mW5 mW2 mW Figure 2: Data rate vs Power
  7. 7.  Hardware Architecture  Software Architecture Hardware Architecture of BAN  Devices used:  Sensor node  Actuator node  Personal device
  8. 8.  Sensor Node:  Gathers data on physical stimuli, processes the data if necessary and reports this information wirelessly. Consists of several components:  Sensor hardware  A power unit  A processor, memory and  A transmitter or transceiver. Eg : i Rhythm
  9. 9.  Actuator Node:  Acts according to data received from sensors / through interaction with the user.  Components similar to sensors:  Actuator hardware (e.g. hardware for medicine administration, including a reservoir to hold the medicine)  A power unit, a processor, memory and  A receiver or transceiver
  10. 10.  Personal Device:  Gathers all the information acquired by the sensors and actuators  Informs User (i.e. the patient, a nurse, a doc etc.) via an external gateway, an actuator or a display/LEDS on the device.  Components:  A power unit, a (large) processor, memory and a transceiver.  Also called a Body Control Unit (BCU) , body gateway or a sink.  E.g.: PDA
  11. 11.  Software have a well-defined interface to integrate hardware and application programs.  Software include three levels: 1. Firmware, 2. OS And 3. Application Software Stacks.  OS can be Symbian OS, Android OS, Blackberry OS, Windows mobile etc.
  12. 12.  Heart Failure (congestive)  Heart Rhythm Management  Bradycardia - beating too fast  Tachycardia - too slow  Atrial Fibrillation or AFib - irregularly.  Hypertension  Diabetes  Parkinson’s Disease  Epilepsy  Mood detection / Depression  Pain Management Applications in Healthcare:
  13. 13.  Pacemaker  Implantable Cardioverter Defibrillator (ICD)  Spinal Actuators  Insulin pump  Continuous Glucose Monitoring  Deep brain stimulator  External & Implantable Hearing Aids - cochlear implant  Retina implants  Muscular signal replacement
  14. 14. Figure 4: MobiHealth system, monitoring a patient outside the hospital environment Fig 3: TMSI Device “Mobi”
  15. 15.  Problems with the use of this technology could include:  Security:  Interoperability:  System devices:  Invasion of privacy:  Sensor validation:  Data consistency:  Interference:  Data Management:
  16. 16.  This presentation demonstrates the use of Wearable and implantable Wireless Body Area Networks as a key infrastructure enabling unobtrusive, constant, and ambulatory health monitoring.  This new technology has potential to tender a wide range of assistance to patients, medical personnel, and society through continuous monitoring in the ambulatory environment, early detection of abnormal conditions, supervised restoration, and potential knowledge discovery through data mining of all gathered information.  The role of Body sensor networks in medicine can be further enlarged and we are expecting to have a feasible and proactive prototype for wearable / implantable WBAN system, which could improve the quality of life. Conclusion:
  17. 17.  U. Varshney, "Pervasive Healthcare and Wireless Health Monitoring," Mobile Networks and Applications, vol. 12, pp. 113-127, March 2007.  Schmidt et al., "Body Area Network BAN--a key infrastructure element for patient-centred medical applications, " Biomedizinische Technik. Biomedical engineering 2002, p365-368  L. Huaming and T. Jindong, "Body Sensor Network Based Context Aware QRS Detection," in Pervasive Health Conference and Workshops, Innsbruck, Austria, 2006, pp. 1-8.  J. Luprano, J. Sola, S. Dasen, J. M. Koller, and O. Chelelat, "Combination of Body Sensor Networks and On-body Signal Processing Algorithms: the Practical Case of MyHeart Project," in International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2006), Cambridge, MA, USA, 2006.  S. A. Taylor and H. Sharif, "Wearable Patient Monitoring Application (ECG) using Wireless Sensor Networks," in 28th Annual International Conference on the IEEE Engineering in Medicine and Biology Society, New York, NY, USA, 2006, pp. 5977-5980.

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