Wikipedia

User:Hobit/Body Sensor Networks

< User:Hobit

"Use body as the media and a source of inspiration, energy to provide long-term, continuous sensing and monitoring – GZ Yang" [1] The term BSN - Body Sensor Network[2][3][4][5][6] was coined to harness several allied technologies that underpin the development of pervasive sensing for healthcare, wellbeing, sports and other applications that require “ubiquitous” and “pervasive” monitoring of physical, physiological, and biochemical parameters in any environment and without activity restriction and behaviour modification. Key to the development of BSN are technologies that address miniaturised biosensor design suitable for both wearable and implantable devices, biocompatibility and materials to ensure long-term deployment, low-power wireless communication, integrated circuits and systems, power scavenging techniques from the body, autonomic sensing and standards and integration. Major technical hurdles of BSN are related to continuous sensing and monitoring, requiring long-term stability of the sensors and low-power operation, also necessitating bio-inspired design (e.g. bio-inspired mix-signal ASIC) and power scavenging techniques ultimately for batteryless operation. For device level inter-connectivity, BSN can be wired (e.g., interconnect with smart fabric) or wireless (making use of common wireless sensor networks and standards, e.g., BAN [7], WPAN (IEEE 802.15.6), Bluetooth/Bluetooth Low Energy (BLE), and ZigBee). Emerging techniques also explore cellular level signalling and smart particles combined with novel sensing and imaging techniques, thus forgoing implantable devices for in vivo sensing.

BSN Technologies


Key Technological Focuses of BSN edit

Biosensor Design edit

Key to the development of BSN technologies is effective biosensor design, which includes novel bioelectrical, biochemical, biophysical, and mechanical sensors. It leverages recent advances in MEMS and microfluidics fabrication with specific focuses on reliable biosystem access and sampling, stable, reagentless operation and readout, as well as device miniaturisation to facilitate deskilling of complex assay formats.

Biocompatibility and Materials edit

Biocompatibility and material selection are important to long-term stability of BSN devices, particularly for implantable sensors. Bio/chemical sensors are subject to significant drift as a result of surface fouling by biological macromolecules and cells [8] . The packaging of sensors for both the protection of vulnerable chemistries and the reduction of surface biofilms warrants materials and membrane design to engineer both a change to surface chemistry and to the solute selectivity of the trans-membrane transport process [9]. Low volume samples, especially, will need interfacing membranes that allow for ‘clean’ operation of the device and a maintained free flow of sample.

Sensor Communication edit

Whilst the use of common wireless sensor networks and standards, e.g., BAN [7], WPAN (IEEE 802.15.6), Bluetooth/Bluetooth Low Energy (BLE), and ZigBee, are useful for device level integration of wearable BSN nodes, much effort of the BSN community is directed to low power wireless data paths and radio propagation within/around the body considering dispersive human tissues. These include low-power inductive coupling, surface guided wave antenna to improve on-body communication, flexible/textile conformal antenna design, as well as the use of narrowband and ultra wideband communication systems. In order to design power efficient in-body communication schemes, understanding the mechanism of wave propagation and attenuation inside human body is important. Accurate modelling of induced electromagnetic fields and propagation in the body is a prerequisite to the design of wearable and implantable wireless sensors [10]

Low Power Design and Power Scavenging edit

Ultra-low power design, in terms of signal conditioning, on-node processing, and wireless communication links, is essential in making BSN platforms truly pervasive and suitable for continuous sensing and monitoring without activity restriction and behaviour modification. To this end, it is necessary, for example, to development ultra-low power mixed-signal ASIC with digital control, analogue processing, or other bio-inspired ASIC designs to drive down the power budget [11]. Power scavenging (harvesting) techniques for BSN is an intensive research area, although still in its infancy for practical use. High capacity power MEMS based on low-frequency motion, for example, has been demonstrated to be able to meet the current processing demand of BSN nodes.

Autonomic Sensing edit

For practical deployment of BSN platforms particularly under extreme environments and motion, issues concerning resource management, dynamic reconfigurability under varying physical conditions and changing environments, adaptive error recovery, and seamless integration and scalability with ambient sensors and wireless infrastructure are important to the truly pervasive deployment of the technology. In this regard, many of the principles of autonomic sensing in terms of self-* properties (self-management, self-configuration, self-optimisation, self-healing, self-protection, self-adaptation, self-integration and self-scaling) to form autonomic sensor networks are particularly attractive. Other topics include context awareness[12], multi-sensor data fusion, data inferencing, knowledge discovery and prediction. The human body responds to and interacts with the external environment in an intricate manner, involving the complex interplay of physical, metabolic and stress related bio-responses. Reliable information about the context under which the biological and physical signals are collected is important.[13]

Standards and Integration edit

Standards and integration are emerging areas of development for BSN. These include: standards and light-weight communication protocols [14], integration with ambient sensing with applications in smart dwellingsand home monitoring, quality of service, trust and security issues, and hardware considerations[15] including low power RF transceiver, energy scavenging[16], battery technology, miniaturisation, system integration, process and cost of manufacturing. For long-term continuous sensing, different sensor embodiment formats to ensure comfort and user compliance are important issues to consider. For example, extensive effort has been direct to smart textiles through the incorporation of a diverse range of conducting elements encompassing polymers, carbon nanotubes, piezoelectric materials and traditional metal conductor combinations (Ag, Ni, Al). Resistive, capacitive and inductive signalling modalities are also used with further conditioning through textile and weave design.

Applications of BSN edit

Recent applications and practical deployment of BSN include elderly care[17], managing patients with chronic diseases[18][19], rehabilitation[20], sports[21][22], and entertainment.[23][24] Extensive effort has also been directed to wearable and implantable sensor integration and development platforms.[25][26][27]

Related Research Projects edit

  •   ESPRIT(Elite Sport Performance Research in Training)[28] - To develop pervasive sensing technologies for professional sport training, well being and healthcare applications.
  •   CAPSIL (Common Awareness and Knowledge Platform for Studying and Enabling Independent Living)[29] - To provide information and resources on research and technologies for independent living for the aging population.
  •   SESAME (SEnsing for Sport And Managed Exercise)[30] - To develop and apply wireless sensor technologies to provide real-time feedback for enhancing the performance of elite athletes.
  •   DARPA (ASSIST Advanced Soldier Sensor Information System and Technology)[31] - To develop an integrated system with body worn sensors to assist soldiers in capturing, reporting and sharing information in the field.
  •   WASP (Wirelessly Accessible Sensor Populations)[32] - To develop a software platform for facilitating the development of wireless sensing applications, such as healthcare, automotive and livestock monitoring applications.
  •   MIT Project Oxygen[33] - To demonstrate pervasive and human-centered computing technologies.
  •   UbiMon (Ubiquitous Monitoring Environment for Wearable and Implantable Sensors)[34] - To develop ubiquitous sensing technologies for wearable and implantable sensors for healthcare applications.
  •   MIThril[35] - To develop wearable computing technologies and investigate novel human-computer interactions for body-worn applications
  •  BASUMA[36] - To investigate and develop Body Area System technologies for ubiquitous multimedia applications.
  •   HUMAN++[37] - To develop wearable sensing technology for long term physiological monitoring of patients.
  •   MobiHealth[38] - To integrate wearable sensors into mobile value added services in the area of health, based on 2.5 and 3 G technologies.
  •   Wealthy[39] - To develop an intelligent support system which will assist patient rehabilitation or people working in extreme environment based on computing and smart sensing technologies.
  •   CodeBlue[40] - To develop wireless sensor network technologies for emergency care.
  •   Smart-Its[41] - To develop platform technology for pervasive computing.


Further Reading edit


References edit

  1. ^ http://www3.imperial.ac.uk/newsandeventspggrp/imperialcollege/centres/roboticsurgery/newssummary/news_13-6-2011-10-50-50
  2. ^ http://news.cnet.com/8301-27083_3-10323325-247.html
  3. ^ http://www.startribune.com/business/90362829.html?page=2&c=y
  4. ^ B. P. L. Lo, S. Thiemjarus, R. King, and G.-Z. Yang, "Body Sensor Network – a wireless sensor platform for pervasive healthcare monitoring," the Third International Conference on Pervasive Computing. Munich, Germany, pp. 77-80, 2005
  5. ^ Hao Y, Foster R. Wireless body sensor networks for health-monitoring applications Physioilogical Measurement 2008; 29(11).
  6. ^ V. M. Jones, V. Gay, and P. Leijdekkers, "Body sensor networks for Mobile Health Monitoring: Experience in Europe and Australia," Proceedings of the Fourth International Conference on Digital Society St. Maarten, Netherlands Antilles, pp. 204-209, 2010.
  7. ^ a b http://www.cs.wustl.edu/~jain/cse574-08/ftp/ban/index.html
  8. ^ Vadgama P Modifying surfaces and interfaces Med Device Technol 22 (2007)
  9. ^ Wisniewski N and Reichert M Methods for reducing biosensor membrane fouling Colloid and Surf B 18, 197 (2000)
  10. ^ Reusens E., Joseph W., Vermeeren G., Martens L. "On-body measurements and characterization of wireless communication channel for arm and torso of human", BSN2007, pp. 264 - 269
  11. ^ http://www.wired.co.uk/magazine/archive/2011/11/features/the-data-will-see-you-now?page=all
  12. ^ B. T. Korel and S. G. M. Koo, "A survey on context-aware sensing for body sensor networks," Wireless Sensor Network, vol. 2, pp. 571-583, 2010.
  13. ^ Body Sensor Networks - Yang, Guang-Zhong (Ed.), Springer, 2006
  14. ^ H. Li and J. Tan, "An ultra-low-power medium access control protocol for body sensor network," Proceedings of the IEEE-EMBS International Conference of the Engineering in Medicine and Biology Society, Shanghai, China, pp. 2451-2454 2005.
  15. ^ http://www.capsil.org/capsilwiki/index.php/BSN_Architectures
  16. ^ V. Leonov, C. Van Hoof, and R. J. M. Vullers, "Thermoelectric and hybrid generators in wearable devices and clothes," Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks, Berkeley, CA, USA, pp. 195-200, 2009.
  17. ^ Cong-Zhi Wang Yong-Ping Zheng,"Home-Telecare of the elderly living alone using an new designed ear-wearable sensor", In Proceeding of BSN 2009, pp.71-74, 2009
  18. ^ http://www.efytimes.com/e1/creativenews.asp?edid=73192
  19. ^ S. Patel, K. Lorincz, R. Hughes, N. Huggins, J. Growdon, and D. Standaert, "Monitoring motor fluctuations in patients with Parkinson’s disease using wearable sensors," IEEE Transactions on Information Technology in Biomedicine, vol. 13, 2009.
  20. ^ H. Ghasemzadeh, R. Jafari, and B. Prabhakaran, "A body sensor network with electromyogram and inertial sensors: multimodal interpretation of muscular activities " IEEE Transactions on Information Technology in Biomedicine, vol. 14, pp. 198-206, 2010.
  21. ^ Julien Pansiot, Benny Lo and Guang-Zhong Yang, "Swimming Stroke Kinematic Analysis with BSN", In Proceeding of the 2010 International Conference on Body Sensor Networks (BSN 2010), Singapore, pp.153-158, June 2010.
  22. ^ Sturm, D. Yousaf, K. Eriksson, M., "A Wireless, Unobtrusive Kayak Sensor Network Enabling Feedback Solutions", In Proceedings of BSN 2011, pp.159-163, 2011
  23. ^ http://www.cms.livjm.ac.uk/pri/otherprojects.html#A_Body_Sensor_Network_and_Gaming_Platform_for_Dynamically_Adapting_Physiotherapy_Treatments
  24. ^ J. A. Paradiso, K. Hsiao, A. Y. Benbasat, and Z. Teegarden, "Design and implementation of expressive footwear," IBM Systems Journal, vol. 39, pp. 511-529, 2000.
  25. ^ http://www3.imperial.ac.uk/newsandeventspggrp/imperialcollege/newssummary/news_25-6-2008-9-34-37
  26. ^ http://www.iom3.org/news/implantable-blood-pressure-sensor
  27. ^ http://ubimon.doc.ic.ac.uk/bsn/public/mcleod-slides.ppt
  28. ^ http://www.esprit-sport.org/
  29. ^ http://www.capsil.org/
  30. ^ http://www.sesame.ucl.ac.uk
  31. ^ http://www.darpa.mil/Our_Work/I2O/Programs/Advanced_Soldier_Sensor_Information_Systems_and_Technology_%28ASSIST%29.aspx
  32. ^ http://www.wasp-project.org/
  33. ^ http://www.oxygen.lcs.mit.edu/
  34. ^ http://www.doc.ic.ac.uk/vip/ubimon/home/index.html
  35. ^ http://www.media.mit.edu/wearables/mithril/
  36. ^ http://www.basuma.de/index.php?lang=en
  37. ^ http://www.imec.be/ScientificReport/SR2007/html/1384156.html
  38. ^ http://www.mobihealth.org/
  39. ^ http://wealthy-ist.com/
  40. ^ http://www.eecs.harvard.edu/%7Emdw/proj/codeblue/
  41. ^ http://www.smart-its.org/
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