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Patient Location via Received Signal Strength (RSS) Analysis Dan Albano, Chris Comeau, Jeramie Ianelli, Sean Palastro Project Advisor Taib Znati Tuesday April 3 rd 2007. Background.

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  1. Patient Location via Received Signal Strength (RSS) Analysis Dan Albano, Chris Comeau, Jeramie Ianelli, Sean PalastroProject Advisor Taib ZnatiTuesday April 3rd 2007

  2. Background • Received signal strength (RSS) – parameter of wireless communications protocols (RF, IR, Bluetooth, etc.) that describes the power of a received signal • Signal strength is proportional to 1/d2 • Using this information, the distance between signal transmitter and receiver can be found • When combined with known locations of reference transmitters, a user’s location can be determined

  3. Project overview • The RSS location system is a set of software tools that allow for continuous indoor user tracking • Rmapr, a radio map generator • Generates a location-specific radio map • Trakr, the main end-user program • Compares real-time RSS values with radio map data to approximate location

  4. Project goals/rationale • Develop an architecture to advance/explore the field of location-aware computing • open-source • non-proprietary • RSS-based • Cross-platform approach allows for virtually limitless applications • Take advantage of 802.11b infrastructure • Existing • Inexpensive • Drivers available • Potential uses • Health Care - Patient monitoring • Military - Infantry / Supply monitoring • Elderly Support - In-Home / Assisted Living

  5. Competitive Analysis • As compared to commercial location tracking solutions, • Our strengths • Open source • Software-based • Free • Non-proprietary • Our weaknesses • Location-specific • Accuracy dependent on building geometry • As location changes, radio map must be updated

  6. Design Alternatives • Wireless communication protocol? • RF, Wi-Fi, Bluetooth, ultrasound, IR • Wi-Fi (802.11b) was chosen • Inexpensive, near-ubiquitous • Programming language? • C/C++, Java, Python, Matlab • C++ was chosen • Simple to implement, pre-existing device libraries • Development platform? • Windows, Linux, Palm OS

  7. Milestones • Wireless access points and Palm units received • Driver development • Linux • Windows • Palm • Blueprints received • Radio-map development • Compile an API for Windows, Linux, Palm • Explore alternate algorithms, improve radio map density • Additional features/refinement

  8. List of Materials • Blueprint or dimensions of 5th Floor of Sennott Square • Device driver capable of extracting RSS values of multiple SSIDs • Five 802.11b compatible access points • Laptop w/ Windows XP, Linux • Microsoft Visual Studio • Windows Driver Development Kit • Palm OS Developer’s Suite • Palm Tx handhelds • Server Desktop Computer

  9. Main components • Hardware • Pre-existing 802.11b infrastructure • At least 3 AP’s • Software • Rmapr • Radio map • Trakr

  10. Hardware – Access Points Our testing area: • 5th floor Sennott Sq. • 5 AP’s • Sources of interference: • Pre-existing wireless networks in addition to our own • Offices, construction materials, wireless devices, etc.

  11. Software – Rmapr Radio Map Generator • Creates radio map of a location by recording RSS values of reference AP’s at many points in the area • Generates a list with the form (x, y, RSS1, RSS2, RSS3, RSS4, RSS5) • As map density increases, accuracy increases, but set-up time increases as well • Radio map is stored in server

  12. Software - Trakr end-user interface • As user moves, the software reads the RSS values of nearby AP’s • These RSS values are compared to the radio map • The closest match from the radio map is loaded and the location data is read • This data is interpreted by the software and updated in the GUI

  13. Viterbi algorithm • The Viterbi algorithm allows us to predict the path and location of the user from the observed changes in signal strength • Makes use of a moving average estimation • In depth discussion of theory is out of the scope of this presentation

  14. Project management • Goals of BIOENG 1160: • Develop radio map • Develop server and end-user programs • Test multiple location algorithms/add functionality • Results of BIOENG 1161 • Develop radio map  • Develop server and end-user programs  • Test multiple location algorithms/add functionality – in progress

  15. Individual areas of focus • Dan Albano • Linux/Palm implementation, driver development • Chris Comeau • Linux/Palm implementation, driver development • Jeramie Ianelli • Literature, research, hardware • Sean Palastro • Windows implementation, driver development

  16. Problems • Initial API tests failed • Further development of project rests on driver development • Drivers eventually developed/adapted • Additional hardware will be required to develop a LINUX platform driver • Additional laptop for LINUX development acquired • Waiting on hardware

  17. Future work • Project refinement • Find errors using multiple methods of location detection, (Averaging, etc.) • Develop location-based error tracking graph to locate areas with significant interference/attenuation • Project expansion • Add GUI front-end • Additional Palm development • Misc. features

  18. Acknowledgements Our group would like to thank • Dr. Taib Znati for his time, effort, and funding • Bill Hoffman for network access and troubleshooting • Anandha Gopalan for Linux advice and code troubleshooting • Bioengineering department for funding and resources

  19. Questions

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