1. Using Mobile Phones to Determine Transportation Modes 2011.04.11 Hyeong-il Ko Sasank Reddy et al., ACM Transactions on Sensor Networks, Vol. 6, No. 2, Article 13, Feb 2010

2. S FT COMPUTING @ YONSEI UNIV. KOREA Contents Introduction Related Work Approach Experimental Setup Results Conclusion 1

3. S FT COMPUTING @ YONSEI UNIV. KOREA Introduction Mobile phones –Computation, sensing, and communication capabilities –Carried by people throughout the day Target applications for transportation mode inference and location information –Physical Activity Monitoring –Personal Impact and/or Exposure Monitoring –Transportation and Mobility-Based Recruitment 2

4. S FT COMPUTING @ YONSEI UNIV. KOREA Related Work 3

5. S FT COMPUTING @ YONSEI UNIV. KOREA Approach Design Goal –User convenience 4 properties of suggested system to be convenient –Contained in one sensing unit –Flexible in terms of the position and orientation –Able to work for a variety of users without additional training –Not reliant on external spatial or user history based indexes Contribution –Suggested classifier that uses information from an accelerometer and a GPS –Able to run on a commodity mobile device 4

6. S FT COMPUTING @ YONSEI UNIV. KOREA Approach Sensor Selection –Bluetooth Not ubiquitous in outdoor settings –Static Bluetooth beacons mainly exists indoor settings Difficult to distinguish if an individual is moving Difficult to distinguish if an environment is changing –Other people carrying devices are moving 5

7. S FT COMPUTING @ YONSEI UNIV. KOREA Sensor Selection –WiFi and GSM Not discriminative when speed profiles are similar –Slow moving traffic, biking, and walking Depends on the density network and end points 6

8. S FT COMPUTING @ YONSEI UNIV. KOREA Sensor Selection –Accelerometer and GPS 10~20% accuracy dropped if only 1 sensor of them is used Negligible 0.6% improved when 4 sensing modalities are used 7

9. S FT COMPUTING @ YONSEI UNIV. KOREA Feature Selection –Window Size A window of 1 sec –Type of Features Accelerometer –Magnitude of the force vector from 3 axises –Mean, variance, energy, and DFT energy coefficient between 1-10Hz Speed –Value directly used from GPS receiver Noise filtering step –Discarding GPS points deemed invalid –Excluding accelerometer data if too few samples are received for classification 8

10. S FT COMPUTING @ YONSEI UNIV. KOREA Feature Selection Selection Method –CFS(Correlation based Feature Selection) Feature subset selector that eliminates irrelevant and redundant attributes Feature subset –Variance along with DFT energy coefficients between 1-3Hz –Speed from the GPS receiver 9

11. S FT COMPUTING @ YONSEI UNIV. KOREA Classifier Selection Classifiers –Instance Classifiers E.g. C4.5 DT, KMC, NB, NN, and SVM –CHMM(Continuous HMM) Output symbols : independent multi-variate Gaussian distributions Hidden states : classification classes Transition probability –Instance based classifier + DHMM(Discrete Hidden Markov Model) DHMM output symbols : instance-based classifications Hidden states : classification classes State transition probabilities 10

12. S FT COMPUTING @ YONSEI UNIV. KOREA Experimental Setup Hardware Platform –Nokia n95 CPU : 332 MHz ARM processor RAM : 128MB 3 axis accelerometer that can sample at 32 Hz Built-in GPS receiver that can sample at 1 Hz WiFi radio that can scan at 0.33 Hz GSM cell radio that can sample at 1 Hz Bluetooth radio that can scan at 0.08 Hz Battery : 950 mAh OS : Symbian S60 3 rd Edition Software Setup –Weka Machine Learning Toolkit –Generalized Hidden Markov Model library –Python 11

13. S FT COMPUTING @ YONSEI UNIV. KOREA Experimental Setup Data Collection –Volunteers to obtain data set 16 individuals –8 male + 8 female –The ages of 20-45 –Accelerometer, GPS, WiFi, and GSM information obtained –How to collect data 1.25 hrs of data per position per individual –Positions : Still, Walk, Run, Bike, Motor, All Total 120 hrs 12

14. S FT COMPUTING @ YONSEI UNIV. KOREA Results Classification Accuracy 13

15. S FT COMPUTING @ YONSEI UNIV. KOREA Results Structure of Overall Classifier 14

16. S FT COMPUTING @ YONSEI UNIV. KOREA Results Device Placement Variation –Mobile phones are often carried at different positions –Classifier is trained on data from all 6 positions Arm, bag, chest, hand, pocket, and waist 15

17. S FT COMPUTING @ YONSEI UNIV. KOREA Results Extended Transportation Mode Traces –Would the DT+DHMM classifier perform in “everyday” use? –1 of the volunteers carried the mobile phone over 4 weeks documented instances of each of the transportation modes 16

18. S FT COMPUTING @ YONSEI UNIV. KOREA Results Extended Transportation Mode Traces(Cont’d) –Would the DT+DHMM classifier perform in challenging urban environment? –The volunteer Carried the mobile phone 3.5 hrs in urban canyons At least 30 mins for each transportation mode –Result Average accuracy : 92.6% Still, walking, and motorized transport : 95% Biking and running state : around 88% 17

19. S FT COMPUTING @ YONSEI UNIV. KOREA Results Memory and CPU Benchmarks –Using Nokia Energy Profiler 20 mins trials were performed 18

20. S FT COMPUTING @ YONSEI UNIV. KOREA Results Energy Consumption 19

21. S FT COMPUTING @ YONSEI UNIV. KOREA Results Energy-Aware Detection –Authors’ objective To create a transportation classifier that captures the behavior of individuals when they are outside –The classifier should be energy efficient –The most effective method to determine when the user is outdoors again GPS –Sampling GPS for the purpose is power hungry –Trigger approach proposed Attempting to sample the GPS when only changes occur to the primary GSM cell tower would be more efficient in terms of energy usage GSM cell towers are used to determine the start of outdoor trips Filter to eliminate the “ping pong” effect 20

22. S FT COMPUTING @ YONSEI UNIV. KOREA Results –Trigger approach proposed(Cont’d) To test the performance of the GSM triggered approach, –16 individuals labeled indoor/outdoor status and collected GSM cell tower every 1 sec for a day Total time of the day trace data collection –Average : 23.2 hrs –Mininum time : 20.7 hrs –Maximum time : 26.8 hrs Corresponding outdoor time –Average : 3.09 hrs –Maximum : 12.0 hrs –Minimum : 0.93 hrs The average percentage of outdoor time identified –91.5% 12.4% energy save compared to GPS 21

23. S FT COMPUTING @ YONSEI UNIV. KOREA Conclusion Transportation mode classification system –Distinguishing between being stationary, walking, running, biking and in motorized travel –Employing a DT followed by a DHMM –Using a mobile phone equipped with a GPS receiver and an accelerometer –Convenient for a user By not having strict position and orientation requirements –Achieving a high accuracy level(93.6%) Based on a dataset of 120 hrs of data from 16 users –Not relying on external spatial indexes –Working well without user-specific training information 22