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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

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

contents
Contents
  • Introduction
  • Related Work
  • Approach
  • Experimental Setup
  • Results
  • Conclusion
introduction
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
approach
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
approach6
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
sensor selection
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
sensor selection8
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
feature selection
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
feature selection10
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
classifier selection
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
experimental setup
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
experimental setup13
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
results
Results
  • Classification Accuracy
results15
Results
  • Structure of Overall Classifier
results16
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
results17
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
results18
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%
results19
Results
  • Memory and CPU Benchmarks
    • Using Nokia Energy Profiler
      • 20 mins trials were performed
results20
Results
  • Energy Consumption
results21
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
results22
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
conclusion
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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