1. PeopleTones: a system for the detection and notification of buddy proximity on mobile phones Kevin A. Li Timothy Sohn Steven Huang William G. Griswold University of California, San Diego

2. phones offer tons of information

3. Crappy sensors Phones are great for context sensing But poor context sensing leads to information flood

4. only two states, nearby and far away (> 0.2mi) when a buddy is near, play their song if phone is in vibrate mode, vibrate the equivalent PeopleTones

5. MOTIVATING EXAMPLES Why do we need buddy proximity? – Meeting up for dinner/drinks (pic of friends at a bar)

6. privacy-friendly proximity detection algorithm technique for reducing sensor noise and power consumption method for generating peripheral cues contributions

7. proximity detection

8. Related works Loopt/Dodgeball Placelab Skyhook(iphone)

9. requirements not annoying -> low false positives since it’s nice to know, doesn’t have to detect all the time -> missing a few true positives is ok

10. a first stab… use a GSM phone to record cell towers it sees every 5 minutes 3 GSM phones, keep 1 stationary try at a variety of distances

11. algorithm a and b are the sets of cell towers seen by each phone

12. initial measurements

13. evaluating proximity algorithm

14. the dataset used the dataset collected by from wardriving seattle 1 [Chen et. al., 2006]

15. coverage

16. precision – number of near reports that are correct divided by the total number of near reports. high precision => few false positives recall – number of near reports that are correct divided by the total number of actual near occurrences high recall => most of the near occurences have been detected what metric do we use?

18. Precision for different “nearby” in suburb Precision for different “nearby” in downtown

20. Precision at different distances in suburb Precision at different distances in downtown

22. reducing sensor noise

23. potential approaches 2-same-filter – Wait for 2 consecutive-same-reqadings – Too many false positives 3-same-filter – Too much delay – Wait for 3 consecutive-same-readings

24. 2-bit counter

25. evaluation

26. generating vibrations

27. measuring vibrations

28. zzz z z z vibrotactile signals

29. mapping music to vibrations

30. capturing the essence of music

31. high level approach just using beat doesn’t always work mapping lyrics doesn’t work well

32. remove noise isolate 6.6kHz to 17.6kHz components using 8 th order Butterworth Filter use amplitude threshold, to keep only components greater than the average

33. take running sum take running sum of absolute value, generate 1 value every 20ms this keeps length consistent

34. exaggerate features compose output from previous step with power function: Ax n,x is sample, A and n are constants, 10<=A<15, 1<=n<=2

35. PLAY SOME MUSIC PLAY VIBRATION PLAY ANOTHER MUSIC PLAY ANOTHER VIBRATION NOTE: PACK A PHONE WITH VIBRATIONS FOR DEMOS

36. only two states, nearby and far away when a buddy is near, play their song if phone is in vibrate mode, play a matching vibrotactile sequence PeopleTones

37. what did you do?

38. participants 3 groups of friends, 2 weeks

39. did you act on the cue?

40. could you tell who it was?

41. user response to the cue

42. Could you tell who it was?

43. lessons cues in the wild should be music higher comprehension rate when users select their own cues obtrusiveness of music cues was not a concern mapping music to vibration was most successful for people who knew the songs well semantic association is key

44. Next steps

45. High level idea

46. Extra stuff