1. Zhiphone: A Mobile Phone that Learns Context and User Preferences Jing Michelle Liu Raphael Hoffmann CSE567 class project, AUT/05

2. CSE567 project2 12/8/2005

3. CSE567 project3 12/8/2005 Objective Adapt the alarm type of a mobile phone to its context.

4. CSE567 project4 12/8/2005

5. CSE567 project5 12/8/2005 Objective Adapt the alarm type of a mobile phone to its context and the preferences of its user.

6. CSE567 project6 12/8/2005 This is what you want Track context Receive call User reaction Learn user preferences

7. CSE567 project7 12/8/2005 Outline Related Work Architectural Overview Feature Extraction Learning Context Learning User Preferences Future Work

8. CSE567 project8 12/8/2005 Related Work SenSay: A Context-aware mobile phone Four states: uninterruptible, idle, active and normal Five functional modules: the sensor box, sensor module, decision module, action module and phone module Sensors include light, motion and microphone Query sensor data and the electronic calendar of the user Use thresholds to classify into different states There is no learning from user preferences

9. CSE567 project9 12/8/2005 Related Work SenSay: A Context-aware mobile phone Four states: uninterruptible, idle, active and normal Five functional modules: the sensor box, sensor module, decision module, action module and phone module Sensors include light, motion and microphone Query sensor data and the electronic calendar of the user Use thresholds to classify into different states There is no learning from user preferences

10. CSE567 project10 12/8/2005 Feature Extraction Architectural Overview Supervised Learning   Learning context Reinforcement Learning Learning user preferences ringing tonevibration offline online voice mail raw data features soft sensors alarm type close to bodyconversationextr. noiseoutdoorphysical act. FFT 1 variance 1 variance 2 derivative 3

11. CSE567 project11 12/8/2005 Feature Extraction potentially useful features running mean running variance derivative exponential smoothing frequency components (FFT) short (10 sec) and long (1 min) running windows 010111011010010011010100011110101010111010010111010100011010101

12. CSE567 project12 12/8/2005 Feature Extraction (cont’d) system of pluggable components (using observer/composition design patterns) raw sensor window filter window filter FFT filter mean filter var. filter window filter mean filter join filter

13. CSE567 project13 12/8/2005 Feature Extraction - Experiments measured execution times on the iPAQ Feature ExtractorTime Mean (window 20)0-1 ms Variance (window 5500)15-18 ms Derivative0-1 ms Exponential Smoothing0-1 ms Fast Fourier Transform (window 4096)1032-1083 ms Window (window 5500, w/ overlap)102 ms

14. CSE567 project14 12/8/2005 Feature Extraction Architectural Overview  Reinforcement Learning Learning user preferences ringing tonevibration online voice mail raw data features alarm type Supervised Learning  Learning context offline soft sensors close to bodyconversationextr. noiseoutdoorphysical act. FFT 1 variance 1 variance 2 derivative 3

15. CSE567 project15 12/8/2005 Learning Context identified five relevant soft sensors Close to Body Conversation Extreme Noise Outdoor Physical Activity Using annotated user traces, we build a Support Vector Machine classifier for each soft sensor offline

16. CSE567 project16 12/8/2005 Learning Context (cont’d) Support Vector Machines + + + + + + + + + + + + + + - - - - - - - Feature 2 Feature 1 ? ? ?

17. CSE567 project17 12/8/2005 Learning Context (cont’d) Support Vector Machines + + + + + + + + + + + + + + - - - - - - - Feature 2 Feature 1 Maximize distance between closest point and boundary (Optimization)

18. CSE567 project18 12/8/2005 Learning Context (cont’d) Support Vector Machines w/ Gaussian Kernel + + + + + + + + + + + + + + - - - - - - - Feature 2 Feature 1 -

19. CSE567 project19 12/8/2005 Learning Context – Experiments recorded and annotated traces at Allen Center AnnotationTrace1Trace2Total Close To Body48 min31 min79 min Conversation24 min5 min29 min Extreme Noise4 min9 min13 min Outdoor6 min10 min16 min Physical Activity5 min 10 min Total recorded time62 min55 min117 min

20. CSE567 project20 12/8/2005 Learning Context – Experiments (cont’d) 2-fold Cross-Validation results (in %) AnnotationPrecisionRecallAccuracy Close To Body88.05887.64782.799 Conversation77.87932.24781.678 Extreme Noise81.56988.54796.511 Outdoor68.47861.20491.874 Physical Activity94.30979.29397.876 Overall82.05969.78890.148

21. CSE567 project21 12/8/2005 Learning Context – Experiments (cont’d) measured execution times on iPAQ and desktop AnnotationClassificationTraining Close To Body127 ms4 sec (±2) Conversationmemory error212 sec (±134) Extreme Noise74 ms3 sec (±2) Outdoor132 ms4 sec (±2) Physical Activity12 ms3 sec (±2) Overall>86 ms45 sec (±28) offlineonline

22. CSE567 project22 12/8/2005

23. CSE567 project23 12/8/2005 Feature Extraction Architectural Overview Supervised Learning  Learning context offline raw data features soft sensors  Reinforcement Learning Learning user preferences ringing tonevibration online voice mail alarm type close to bodyconversationextr. noiseoutdoorphysical act. FFT 1 variance 1 variance 2 derivative 3

24. CSE567 project24 12/8/2005 Learning User Preferences Scenario 1 Soft sensors detect CloseToBody, Conversation Call comes in and phone is ringing User hangs up phone without taking call Scenario 2 Soft sensors detect CloseToBody, Physical Activity Call comes in and phone vibrates User does not notice call big negative reward small negative reward

25. CSE567 project25 12/8/2005 Learning User Preferences Reinforcement Learning (Q-Learning) Neural Network soft sensor states + current alarm type new alarm type

26. CSE567 project26 12/8/2005 What we can do in the future use an open-source VoIP library, e.g. JVOIPLIB, to enable real cell phone capability apply more advanced reinforcement learning on user preferences evaluate the effectiveness of learning user preferences

27. CSE567 project27 12/8/2005 End