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Using Mobile Phones to Determine Transportation Modes

Using Mobile Phones to Determine Transportation Modes. Sasank Reddy et al., ACM Transactions on Sensor Networks, Vol. 6, No. 2, Article 13, Feb 2010. 2011.04.11 Hyeong-il Ko. Contents. Introduction Related Work Approach Experimental Setup Results Conclusion. Introduction.

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Using Mobile Phones to Determine Transportation Modes

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

  2. Contents • Introduction • Related Work • Approach • Experimental Setup • Results • Conclusion

  3. 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

  4. Related Work

  5. 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

  6. 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

  7. 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

  8. 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

  9. Feature Selection • 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

  10. 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

  11. 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

  12. 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 3rd Edition • Software Setup • Weka Machine Learning Toolkit • Generalized Hidden Markov Model library • Python

  13. 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

  14. Results • Classification Accuracy

  15. Results • Structure of Overall Classifier

  16. 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

  17. 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

  18. 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%

  19. Results • Memory and CPU Benchmarks • Using Nokia Energy Profiler • 20 mins trials were performed

  20. Results • Energy Consumption

  21. 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

  22. 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

  23. 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

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