1. Small, Lightweight Speed and Distance Sensor for Skiers and Snowboarders Michael Bekkala Michael Blair Michael Carpenter Matthew Guibord Abhinav Parvataneni Dr. Shanker Balasubramaniam

2. Objective  Introduction  Design Requirements  Proposed Solution  Testing Results  Conclusions and Accomplishments

3. Introduction  Living by the numbers  Measuring and interpreting your performance has never been more valuable  Critical to gain a competitive edge  Most devices are based on repetitive motion but don’t apply to skiing or snowboarding  Nike+  Bicycle speedometer  Goal: Build a speed and distance sensor for skiers and snowboarders

4. Design Requirements Operable in Subzero Temperatures Useable in winter apparel Lightweight – Less than two pounds Cost – less than $500 Battery Life greater than 2 hours Accurately record speed and distance

5. Proposed Solution  Integration of two major systems  Global Positioning System (GPS)  Inertial Navigation System (INS)

6. Inertial Navigation System  Comprised of accelerometers and gyroscopes  Accelerations  Angular Velocities  Requires analog to digital conversion  Careful calibration is required to achieve high accurate readings  Gives great short-term accuracy, but errors grow with time.

7. Global Positioning System  Gives position in terms of longitude, latitude, and altitude  Determine distance, displacement and speed  NMEA 0183 communications standard  Long term reliability, but poor short term accuracy.  Sampled at 1Hz http://en.wikipedia.org/wiki/Gps

8. Kalman Filtering  Advanced sensor integration technique  Uses statistical error measurements to provide a better estimate  Accuracy improves with time  Can use the estimate to regulate INS errors

9. User Interface  Easily accessible menu displayed on LCD  Review Performance  Average and Peak Speed (mph)  Distance and Displacement (miles)  Color changing buttons  Color of button changes depending on their use  Intuitive color scheme aids usability (ie. Red for Stop, Green for Go)

10. Menu Hierarchy

11. PC Interface  Upload run data from device  Can be saved on your PC for performance tracking and reference  Points can be plotted in Google Earth  Visual representation of data using graphs

12. PC Interface Example

13. Successful Failure  Reached beyond design requirements with an innovative solution  Unpredicted complexity of INS and Kalman filter  Hardware implemented, software not ready  Invaluable knowledge gained through:  Research  Trial and error  Despite proposed solution being a successful failure  GPS solution that meets and exceeds design requirements  Extensive user functionality  Store, retrieve, and manage data all on device or through a PC  Higher data sample rate for greater accuracy  Rechargeable battery

14. Kalman Filter Divergence  Difficult to troubleshoot due to 1200+ lines of code  Unsuccessful in determining cause of divergence  Poor Initialization might be to blame  Confident that with more time, root cause could be determined

15. Testing Results  Several Trials  Moving vehicle  On foot  Varying speeds and distances  Peak speed had significant error  One bad sample results in erroneous reading  Average speed had reasonable accuracy based on number of data points  Results verified by digital speedometer and consumer GPS.

16. Conclusions and Accomplishments  GPS Solution  Accurate average and peak speeds  Distance accurate over a large span  Inaccurate over short spans and slow speeds  Robust Display Menu  Allows easy access to data and configuration options  PC Interfacing  Plot a run on Google Earth with the click of a mouse  Plot speed over time to visualize a run

17. Future Considerations  Printed Circuit Board  USB Compatibility  Wireless headset communication via Bluetooth  Smaller form factor  INS integrated through Kalman Filtering