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symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
symbolic object location
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symbolic object location

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  • 1. symbolic object localization through active sampling of acceleration and sound signatures Paul Lukowicz, Kai Kunze 1
  • 2. motivation “The Phone of the Future” The Economist, 2 02-08 2006
  • 3. … no, seriously healthcare applications is a monitoring device on the body? Where is it? (pick it up before you go) Supporting elderly and cognitive impaired people MonAMI 3 12 million Euro budget over 4 years
  • 4. derived requirements infrastructure-less cheap/simple sensors symbolic location interesting the two most likely ‘hits’ help 4
  • 5. approach A mobile phone ringing or vibrating sounds differently depending on where it is. 5
  • 6. approach -abstraction- mechanical stimuli in this case: – narrow frequency ‘beeps’ (high frequency) – vibration (low frequency) analysis high frequency stimulus -high frequency response (over microphone) low frequency stimulus – low frequency response (over accelerometer) – high frequency response (over microphone) 6
  • 7. low frequency stimulus -vibration- vibration acceleration: coupled directly to surface absorption <=> resonance information: fixed vs. free hard vs. elastic vibration sound: sound of device hitting surface depends not only on surface, 7 but on overall structure
  • 8. High frequency stimulus -sound beeps- structure specific closed vs. open material specific absorption well understood in construction and music 8 Table from Olson, H.: Music, Physics and Engineering. (1967)
  • 9. applying the approach two distinct modes: specific location mode + exact location information - need for training data abstract location class + training problem avoided - only fuzzy location information 9
  • 10. issues to consider • microphone and speaker placement – speakers and mics are cheap • variations inside a symbolic location – though luck • number of relevant locations – Room-level location (RF) • sensor requirements – cheap sensors sufficient • complexity – procedure performed seldom 10
  • 11. recognition method -features used- From over 40 features calculated the following 10 are used: • zero crossing rate • median • variance • 75% percentile • inter quartile range • root mean square • frequency range power • sums power wavelet determinant coefficient • number of peaks • peak height sound fingerprint vibration sound vibration acceleartion 11
  • 12. recognition method • sliding window feature extraction – Over 30 standard features calculated, 10 used • 2 frequency features • separate classifiers for each stimulus response – C 4.5, Naïve Bayes, KNN, HMMs etc. – comparable results fp sound vib sound vib accel • fusion techniques: extract features extract features extract features sliding window sliding window sliding window – majority decision classification classification classification – lookup table (using Naïve Bayes) (using Naïve Bayes) (using Naïve Bayes) best two classifications from each lookup table (created by training data) 12 Result
  • 13. fusion fp sound vib sound vib accel extract features extract features extract features sliding window sliding window sliding window classification classification classification (using Naïve Bayes) (using Naïve Bayes) (using Naïve Bayes) best two classifications from each lookup table (created by training data) Result 13
  • 14. experiments data acquisition: Nokia 5500 Sport recognition method: batch processing 2 distinct experimental setups: specific location scenarios: office, living room, apartment abstract location class driven data collection: furniture store 14
  • 15. scenarios 30 samples per location 10 for training 20 for testing living room office apartment 15 9 locations 12 locations 11 locations
  • 16. abstract classes surface types: padding glass iron metal stone wood compartment: Open/closed (except metal) For each type and compartment: 6 different kinds of furniture 12 samples each 16 2 pieces of furniture for training, 4 for testing
  • 17. experiments -’beeps’ used- intensity time 500 1000 1500 2000 2500 3000 3500 time frequency (Hz) Eight distinct sound beeps from 500 -3500 Hz 17
  • 18. audio fingerprint examples backpack drawer intensity time time 18
  • 19. vibration acceleration stereo intensity intensity time bed time using norm of the 3 axis intensity accelerometer 19 time
  • 20. fingerprint and vibration sounds carpet desk 20
  • 21. frequency range power and sums power wavelet determinant coefficient parameters adjusted for each frequency ‘beep’ feature vectors … rms1 frp 500 wavelet 500 rms1 frp 500 wavelet 500 21
  • 22. results 22
  • 23. … including second best 23
  • 24. over 35 specific location (2nd and 3rd best) 24
  • 25. confusion matrix (over all third best) ledges radiator metal table (solid metal, stone) 25
  • 26. abstract class results (2nd best) iron open 26
  • 27. … summing up • best: audio fingerprint • worst: vibration acceleration – dependence of vibration on battery • quality of accelerometer / vibration-motor / microphone • improvements: – audio fingerprint – vibration sampling (changing levels) • suitable abstractions 27 • acceptable for users?
  • 28. ‘Your phone is on the desk!’ 28
  • 29. opportunistic activity recognition (publications, soon: data + code) http://opportunistic.de/ blog about wearable / ubiquitous computing http://wearcomp.eu/ DEMO? 29

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