Small, Lightweight Speed and Distance Sensor
for Skiers and Snowboarders
Michael Bekkala
Michael Blair
Michael Carpenter
Matthew Guibord
Abhinav Parvataneni
Dr. Shanker Balasubramaniam
Objective
 Introduction
 Design Requirements
 Proposed Solution
 Testing Results
 Conclusions and Accomplishments
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
Design Requirements
Operable in
Subzero
Temperatures
Accurately record
speed and distance
Useable in
winter apparel
Cost – less than $500
Lightweight – Less
than two pounds
Battery Life greater
than 2 hours
Proposed Solution
 Integration of two major systems
 Global Positioning System (GPS)
 Inertial Navigation System (INS)
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.
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
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
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)
Menu Hierarchy
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
PC Interface Example
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
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
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.
Conclusions and
Accomplishments

GPS Solution


Accurate average and peak speeds
Distance accurate over a large span


Robust Display Menu


Inaccurate over short spans and slow speeds
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
Future Considerations
 Printed Circuit Board
 USB Compatibility
 Wireless headset communication via Bluetooth
 Smaller form factor
 INS integrated through Kalman Filtering