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

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Presentation on theme: "Detecting Movement Type by Route Segmentation and Classification Karol Waga, Andrei Tabarcea, Minjie Chen and Pasi Fränti."— Presentation transcript:

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 Nokia Android iPhone None 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.

5 Nokia: 50% of its smart phones has GPS by 2010-12. Gartner: 75% has GPS by the end of 2011. Trends and popularity of GPS Current situation Our lab: Nokia847 % Android424 % iPhone0 0 % None530 % 70 %

6 173 users 7,958 routes 5,208,205 points Mopsi route collection 4 th October, 2012

7 Collected GPS route Plot on map

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

9 Collected GPS route Ground truth

10 Collected GPS route Another 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 Movement type classification Movement types considered: Walk Run Bicycle Car Other possibilities: Boat Flight Spatial context needed Skiing Speed? Track location, season Train Bus Time tables

15 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 Rule-based! 2-order Hidden Markov model

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 segmentation Dynamic programming Minimize intra-segment variance: Optimal segmentation: O(n 2 k)

19 Number of segments

20 Move type classification A priori probabilities

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

22 Rule-based model (HMM)

23 Experiments

24 Segmentation of car route

25 Separating stop segments

26 Long distance running Overall statistics from running by move type

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

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

29 What next?

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

31 New movement types Train Skiing Flight

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


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