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Detecting Movement Type by Route Segmentation and Classification

Detecting Movement Type by Route Segmentation and Classification. Karol Waga , Andrei Tabarcea , Minjie Chen and Pasi Fränti. University of Eastern Finland. Joensuu. Joki = a river Joen = of a river Suu = mouth. Joensuu = mouth of a river. Motivation.

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Detecting Movement Type by Route Segmentation and Classification

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  1. Detecting Movement Type by Route Segmentation and Classification Karol Waga, Andrei Tabarcea,Minjie Chen and Pasi Fränti

  2. University of Eastern Finland Joensuu Joki = a river Joen = of a river Suu = mouth Joensuu = mouth of a river

  3. Motivation

  4. Trends and popularity of GPS Previous predictions Nokia: 50% of its smart phones has GPS by 2010-12. Gartner: 75%has GPS by the end of 2011. Nokia Android iPhone None

  5. Trends and popularity of GPS Current situation Nokia: 50% of its smart phones has GPS by 2010-12. Gartner: 75%has GPS by the end of 2011. 70 % Our lab: Nokia 8 47 % Android 4 24 % iPhone 0 0 % None 5 30 %

  6. Mopsi route collection4th October, 2012 173 users 7,958 routes 5,208,205 points

  7. Collected GPS routePlot on map

  8. Collected GPS routeTime-vs-speed 14 12 10 What is the activity? 8 Speed (km/h) 6 4 2 Time

  9. Collected GPS routeGround truth

  10. Collected GPS routeAnother example

  11. Summarization of entire route

  12. Existing solutions

  13. Features and classifiers Sensor data • GPS • GSM, WiFi • Accelerometers • Combination of multiple sensors Classification • Rule-based vs. trained • Fuzzy logic • Neural networks • Hidden Markov model

  14. Run Walk Car Bicycle Boat Flight Bus Train Movement type classification Movement types considered: Other possibilities: Skiing Time tables Speed? Spatial contextneeded Track location, season

  15. Rule-based! 2-order Hidden Markov model Problems attacked Problems addressed: • Training material is not always available • Problem of over-fit • Loss of generalization Limitations of current solution: • Correlation between neighboring segments • Accuracy of segmentation

  16. Proposed solution

  17. Overall algorithm Optimal segmentation: • Minimize intra-segment speed variance • Detect stop segments Move type classification: • Speed features • 2-order Hidden Markov Model

  18. Route segmentationDynamic programming Minimize intra-segment variance: Optimal segmentation: O(n2k)

  19. Number of segments

  20. Move type classificationA priori probabilities

  21. 2nd order Hidden Markov Model Previous segment Cost function: Next segment Cost function:

  22. Rule-based model (HMM)

  23. Experiments

  24. Segmentation of car route

  25. Separating stop segments

  26. Long distance running Overall statisticsfrom running by move type

  27. Interval training Intervals Stops Warm-up &slow-down

  28. Bicycle trip represented as car Algorithm tries to be too clever

  29. What next?

  30. Skiing Boat Flight Bus Train Further improvements More move types Better stop detection Generate ground truth

  31. Skiing Flight Train New movement types

  32. Conclusions Method that (usually) works! Simple to implement No training data required

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