Indoor Localization and Navigation of Wheelchair Users with Smartphones Ruolin Fan, Silas Lam, Emanuel Lin, Oleksandr Artemenkoⱡ, Mario Gerla University of California, Los Angeles (UCLA) {ruolinfan, silaslam, emanuel, gerla}@cs.ucla.edu ⱡIlmenau University of Technology oleksandr.artemenko@tu-ilmenau.de

Outline Introduction Background System Design Implementation Evaluation Conclusion

Introduction GPS does not work indoors Lack of satellite signals Need an alternative way to position ourselves indoors Try to utilize unique features pertaining to wheelchairs Transform measured wheel rotations into both distance and angular displacement Crowd sourcing popular wheelchair access paths Useful for blind/impaired wheelchair riders More motivations: crowd sourcing popular wheelchair access paths Blind/impaired wheelchair riders

Background: Indoor Localization Triangulation methods from cellular, WiFi, or acoustic (Signal strength or signature) Require landmark placement knowledge, previous mapping of the site; affected by obstacles Dead reckoning Compute the current position based on a previously known position and incremental displacement Can complement and rescue GPS and triangulation methods (eg Autogait[Percom 10])

Wheelchair Dead Reckoning - Overview Get initial position of the wheelchair via GPS coordinates or other means Mark the wheels on the wheelchair at each spoke Track the wheelchair’s movements by counting rotations of the wheels using the marks (a “tick”) Simple model (perfect traction, no sliding): If wheels rotate at the same rate => straight movement If wheels rotate at different speeds => turns

Inferring Movements Straight forward movement: Sharp turns: Both wheels move at the same rate cwheel: the wheel’s circumference n: the number of marks on each wheel Sharp turns: One wheel is moving while the other stays still wchair: the width of the wheelchair cchairTurn: The circumference when the chair turns a full circle dtravelled: The distance travelled by the turning wheel

Inferring Movements (Cont’d) General Turns One wheel moves faster than the other Derive equation using radians , And therefore In degrees,

Implementation Wheelchair Specifications 8 magnets per wheel 1 reed switch per wheel Reed switches connected to Bluetooth mouse When magnet moves close to reed switch, it trigger a mouse click event

Implementation (Cont’d) Translate left/right mouse clicks to distance/direction traveled Base calculations on physical wheelchair measurements Implemented straight movement and sharp turns Clicks detected by JavaScript in web browser Events are sent via AJAX to PHP server and MySQL database Visualize wheelchair movement on a map

Implementation Challenges Wheels are not always synchronized together Magnets are far apart from one another Result: coarse-grained data Wheels may “slip” due to physical imperfections

Our Solution (can you explain better please??) Find ways to do “approximately equals” Made our own low-pass filter in counting the clicks Single values that look like (1,0) would behave like (1,1), and pairs like (1,0),(0,1) would also behave like (1,1) Count small turns as straight movements until confirmed to be a turn When a turn is confirmed, backtrack the last forward movement and aggregate the turn Basically this takes care of the problem when you have left and right wheels not having the same number of ticks even when they are moving forward, due to either missed clicks or the wheels being unsynchronized. You can take a look at the “Implementation” section of the paper, with the paragraph beginning with “Unfortunately, this simple design does not work as intended by itself…”, it should explain quite clearly what’s going on here and in the next two slides.

Example Forward +-----------+-------+-----------------+ | time | state | magnitude | | ... | ... | ... | | 855742327 | F | 0.9106 | | 855743328 | F/R | 1.13825 | | 855744328 | F | 0.9106 | | 855745328 | F | 0 | | 855746327 | F/L | 1.13825 | | 855747352 | F | 0.9106 | | 855748332 | F/L | 1.13825 | | 855749332 | F | 0.9106 |

Example Turn +-----------+-------+-----------------+ | time | state | magnitude | | ... | ... | ... | | 855713328 | F/L | 0.22765 | | 855714328 | F | 0.22765 | | 855715329 | L | 49.245283018868 | | 855716329 | L | 24.622641509434 | | 855723329 | L | 24.622641509434 | | 855726328 | F | 0.68295 | | 855727328 | F/L | 0.9106 | Total turn = 98.49 degrees

Evaluation - General Movements Move the wheelchair around Boelter Hall 3rd floor, the main engineering building at UCLA Straight forward movement is accurate Turns are off Only 8 magnets on a wheel: can only measure degrees in increments of 24.5 The closest to a 90 degree turn is 98 degrees

Evaluation – Straight Movements Error Rate vs. Travelling Speed Increasing the travelling speed can cause a decrease in accuracy, but the approximate equality filter greatly increases accuracy Error rate is the absolute value of the difference between actual distance travelled and the measured distance, divided by the actual distance travelled

Evaluation – Straight Movements (Cont’d) Error Rate vs. Update Period (Fast Travelling Speeds) Increasing the update frequency can lead to more accurate results for straight forward movements, both with and without filters

Improving Turn Accuracy assuming blue print is known Right angle correction Assume 90 degree turns when the turning angle is close to it Correction via boundary detection Detect building boundary and make corrections accordingly Projected results Projected results

Conclusions Indoor localization with a wheelchair can be accomplished by translating wheel rotation measurements into distance and direction Accuracy is high for slow to medium speeds, but decreases as speed goes up Improvements can be made by simply adding magnets Successful proof of concept project

Future Work Improve the accuracy by exploiting existing smartphone sensors: Compass, altimeter (in a multilevel building), gyroscope, accelerometer Synergize wheelchair dead reckoning with WiFi signature methods The wheelchair is used as surveyor, to calibrate the signatures

Thank You!