detecting movement type by route segmentation and classification karol waga, andrei tabarcea, minjie...
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![Page 1: Detecting Movement Type by Route Segmentation and Classification Karol Waga, Andrei Tabarcea, Minjie Chen and Pasi Fränti](https://reader035.vdocument.in/reader035/viewer/2022062519/56649e415503460f94b33a2a/html5/thumbnails/1.jpg)
Detecting Movement Type by Route Segmentation and
Classification
Karol Waga, Andrei Tabarcea,Minjie Chen and Pasi Fränti
MOPSIPROJECTMOPSI
PROJECTUNIVERSITYOF EASTERN
FINLAND
UNIVERSITYOF EASTERN
FINLAND
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University of Eastern Finland
JoensuuJoensuuJoki= a riverJoen = of a river
Suu = mouthJoensuu = mouth of a river
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Motivation
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NokiaAndroidiPhone
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:Nokia 8 47 %Android 4 24 %iPhone 0 0 %
None 5 30 %
70 %
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173 users 7,958 routes
5,208,205 points
Mopsi route collection4th October, 2012
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Collected GPS routePlot on map
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What is the activity?
Sp
eed
(km
/h)
Time
14
12
10
8
6
4
2
Collected GPS routeTime-vs-speed
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0 1000 2000 3000 4000 5000 60000
2
4
6
8
10
12
14
time
spee
destimated segment result
Collected GPS routeGround truth
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0 200 400 600 800 10000
5
10
15
20
25
time
spee
destimated segment result
Collected GPS routeAnother 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 contextneeded
Skiing
Speed? Track location, season
Train BusTime
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 segmentationDynamic programming
1
1( )j
j j j
i
i i ij
f t t
( , ) min( ( , 1) ( )), (1... 1)
( , ) arg min ( ( , 1) ( ))
sc s c
sc c s c
D s r D c r t t c s
A s r D c r t t
Minimize intra-segment variance:
Optimal segmentation:
O(n2k)
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0 1 2 1arg min ( ( , ) ( )), 1...i nm D n i i t t i m
Number of segments
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0 10 20 30 40
0.2
0.4
0.6
0.8
1
Speed(km/h)
Pro
bab
ility
BikeRun
WalkStop
Car
Move type classificationA priori probabilities
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2 1 1
1 2
( | , ) ( | )
( )
Mi i i i
i i
P m m m P m Xf
P m
i 1 11
( | X , , )M
i i ii
f P m m m
Cost function:
Cost function:
2nd order Hidden Markov Model
Previous segment
Next segment
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Probability: Prev.
Next
0.6 - - 0.2 0.2 0.5 0.2 - 0.1 0.2 0.5 - 0.2 0.1 0.2 0.5 - - 0.3 0.2 0.8 - - 0.1 0.1 0.5 0.2 - 0.1 0.2 - 0.6 - 0.2 0.2 - 0.4 0.4 0.1 0.1 - 0.4 - 0.4 0.2 - 0.8 - 0.1 0.1
Probability: Prev.
Next
0.5 - 0.2 0.1 0.2 - 0.4 0.4 0.1 0.1 - - 0.4 0.4 0.2 - - 0.4 0.4 0.2 - - 0.8 0.1 0.1 0.5 - - 0.3 0.2 - 0.4 - 0.4 0.2 - - 0.4 0.4 0.2 0.2 0.2 0.2 0.2 0.2 - - 0.1 0.7 0.2 0.8 - - 0.1 0.1 - 0.8 - 0.1 0.1 - - 0.8 0.1 0.1 - - 0.1 0.7 0.2 0.2 0.2 0.2 0.2 0.2
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 statisticsfrom running by move type
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Interval training
Intervals
Warm-up &slow-down
Stops
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Bicycle trip represented as carAlgorithm tries to be too clever
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What next?
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Further improvements
Boat Flight SkiingTrain 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