Indoor Localization and Navigation of Wheelchair Users with Smartphones

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

2. Outline Introduction Background System Design Implementation Evaluation Conclusion

3. 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

4. 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 (egAutogait[Percom 10])

5. 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

6. Inferring Movements • Straight forward movement: • 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

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

8. 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

9. 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

10. 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

11. 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

12. 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 |

13. 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

14. 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

15. Evaluation – Straight Movements Error Rate vs. Travelling Speed

16. Evaluation – Straight Movements (Cont’d) Error Rate vs. Update Period (Fast Travelling Speeds)

17. Improving Turn Accuracyassuming 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

18. 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

19. 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

20. Thank You!