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 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/d 2 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

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

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 b infrastructure Existing Inexpensive Drivers available Potential uses Health Care - Patient monitoring Military - Infantry / Supply monitoring Elderly Support - In-Home / Assisted Living

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

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

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

List of Materials Blueprint or dimensions of 5 th Floor of Sennott Square Device driver capable of extracting RSS values of multiple SSIDs Five b 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

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

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

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, RSS 1, RSS 2, RSS 3, RSS 4, RSS 5 ) As map density increases, accuracy increases, but set-up time increases as well Radio map is stored in server

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

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

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

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

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

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

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

Questions