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

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

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

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

Collected GPS route Plot on map

What is the activity? Speed (km/h) Time Collected GPS route Time-vs-speed

Collected GPS route Ground truth

Collected GPS route Another example

Summarization of entire route

Existing solutions

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

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

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

Proposed solution

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

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

Number of segments

Move type classification A priori probabilities

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

Rule-based model (HMM)

Experiments

Segmentation of car route

Separating stop segments

Long distance running Overall statistics from running by move type

Interval training Intervals Warm-up & slow-down Stops

Bicycle trip represented as car Algorithm tries to be too clever

What next?

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

New movement types Train Skiing Flight

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