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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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University of Eastern Finland Joensuu Joki= a river Joen = of a river Suu = mouth Joensuu = mouth of a river
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Motivation
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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.
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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 %
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173 users 7,958 routes 5,208,205 points Mopsi route collection 4 th October, 2012
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Collected GPS route Plot on map
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What is the activity? Speed (km/h) Time 14 12 10 8 6 4 2 Collected GPS route Time-vs-speed
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Collected GPS route Ground truth
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Collected GPS route Another example
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Summarization of entire route
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Existing solutions
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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
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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
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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
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Proposed solution
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Overall algorithm Optimal segmentation: Minimize intra-segment speed variance Detect stop segments Move type classification: Speed features 2-order Hidden Markov Model
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Route segmentation Dynamic programming Minimize intra-segment variance: Optimal segmentation: O(n 2 k)
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Number of segments
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Move type classification A priori probabilities
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Cost function: 2 nd order Hidden Markov Model Previous segment Next segment
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Rule-based model (HMM)
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Experiments
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Segmentation of car route
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Separating stop segments
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Long distance running Overall statistics from running by move type
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Interval training Intervals Warm-up & slow-down Stops
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Bicycle trip represented as car Algorithm tries to be too clever
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What next?
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Further improvements Boat Flight Skiing Train Bus More move types Better stop detection Generate ground truth
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New movement types Train Skiing Flight
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Conclusions Method that ( usually ) works! Simple to implement No training data required
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