1. Doorjamb: Unobtrusive Room-level Tracking of People in Homes using Doorway Sensors Timothy W. Hnat, Erin Griffiths, Ray Dawson, Kamin Whitehouse U of Virginia Presenter:SY

2. About This Paper Unobtrusive room-level tracking – People in homes Doorway sensors – Ultrasound sensor Method – Estimates the height and direction

3. Technical Problems Multi-target tracking – Data association Noise – Person’s posture, multipath reflections, and the natural undulation of gait Algorithms – Crossing event detection – Tracking

4. Contributions Hardware – Design and prototyping – Lesson learned In-depth analysis of the sources errors – Present signal processing algorithm Data association challenges – Tracking algorithm Proof-of-concept implementation, deployment, and empirical evaluation

5. Outline Hardware design Signal processing algorithm Tracking algorithm Evaluation Conclusion

6. Hardware Features – Cost effective – Battery powered – Wireless Design – Detect height Measure the distance to the top of the head – Detect walking direction Angled into one room more than the other

7. Doorway Sensor Parallax PING ultrasonic range finders Passive infrared sensors Magnetic reed sensors Custom-designed power module Synapse Wireless SnapPY RF100 module

8. Achieving Doorway Coverage Requirements – 1 cm resolution – Heights ranging from 151 cm to 189 cm – Walking speeds up to 3 m/s^2 – Doorways range: 90-300 cm wide, 213-275 cm tall Parallax PING ultrasonic – 40 degree beam angle – Min: 2 cm; Max: 300 cm

9. Achieving Doorway Coverage Tallest person – Gap between the head and doorway  24cm – 40 degree beam  Sensing diameter of 17 cm – Speed of 3 m/s, a head that is 15 cm diameter Pass sensing region in about 100 ms – 50 Hz sample rate – one module at a time

10. Doorway size Typical doorway width of 90 cm – Sensing diameter – 17 cm – Head radius – 7 cm – Two sensors should be enough Higher door frames require fewer sensor 300 x 275 cm – 4 range finders – Sampling rate 12.5 Hz – Cannot support wide and short

11. Early Prototypes and Lessons Learned Audible click

12. Outline Hardware design Signal processing algorithm Tracking algorithm Evaluation Conclusion

13. Signal Processing Input: stream of height value Output: doorway events D (t j,h j, v j ) Four algorithms – Doorway crossing detection – Noise filtering – Height estimation – Direction estimation

14. Signal Captured

15. Doorway Crossing Event Find timeout, multi-path, measurement events Within 400 msecs of each other

16. Noise Filtering Extend 200ms Define clusters

17. Noise Filtering -- Obstacle Extends 30 seconds on either side – Remove any height measurement that is positive and identical

18. Height Estimation Multi-path reflections – Maximum measurement may fail – Typically only occur once Height estimation – If maximum height cluster exist Max of the cluster – Else Maximum height

19. Direction Estimation Sensor tilts into the doorway Three algorithms – Line slope – Compare max height timestamp to median – Compare min height timestamp to median Vote – Each algorithm estimate: +1, -1, 0 – Sum all: [-3,3]

20. Outline Hardware design Signal processing algorithm Tracking algorithm Evaluation Conclusion

21. Tracking Input: sequences of detection events D Output: Corresponding room states S, (r1 i, r2 i ) Ambiguity – False detections, miss detections Key insight – Ambiguities can often be resolved by future observations

22. MHT Algorithm Multiple hypothesis tracking approach – Multiple alternative tracks are considered simultaneously As new events are processed – Tracks that are not consistent with the new information are evicted

23. Overview Initial – All tracks created with identical weight – For 2 persons + K rooms, K 2 tracks are created Update – For each doorway event Update track Update weight (based on prior training study) Merging and Evicting – Evicting low weight tracks – Merging duplicate tracks

24. Prior Training Study Find conditional probabilities – p(H|O) – a height measurement given the origin – p(V|O) – a direction measurement given the origin – p(H = ) – probability of missed detection Origin -- Person A, or B, or false detection Training period – Each individual walks under each doorway multiple times

25. Creating Tracks Initial tracks – every possible combination For each new doorway event – Between rooms i and j – Five new states are possible a/b move to room i/j + false detection – Duplicate every track 5 times

26. Weighting Tracks New weight is – Old weight multiply by – Probability of the origin moved through doorway m given height measurement – Probability of moving from room p to m given the direction measurement – Probability of moving from the last observed room m-1 to p without having detected

27. Merging and Evicting Hypotheses “N-best” eviction policy – Keep the n best tracks Problem – duplicate tracks Track merging algorithm 1 2 3 4

28. Outline Hardware design Signal processing algorithm Tracking algorithm Evaluation Conclusion

29. Experimental Setup Built 43 ultrasonic doorway sensors – Deployed across 4 different homes – Periods of 6-18 months – Used for development, testing, and iterative design For this evaluation – Performed 3 controlled experiments – 3 different pairs of testers – Randomly walk around – Collect ground truth with handheld device – 3000 unique doorway events

30. Evaluation Metric Type 1: correct state Type 2: wrong person Type 3: false room transition Type 4: missed room transition

31. Tracking Accuracy

32. False Detections and Missed Detections Precision: – The number of false detections divided by the number of total detections Recall – Number of missed detections divided by the number of true doorway crossing events

33. Height Measurement Accuracy

34. Direction Measurement Accuracy

35. Systems Performance Average 24 states, max 55 states per track Real time, online – With 500 ms look-ahead window

36. Limitations Fall short of true in-situ experiments – Controlled experiments Do not capture long-term effects A proof-of-concept for Doorjamb tracking Scalability – Typical homes with 3-4 people Requires calibration and training Does not detect children

37. Conclusion Track people in homes with room-level accuracy Unobtrusive Achieve 90% tracking accuracy My opinions – Well written complete work – Not so sexy – Has it’s own selling points