Detecting Movement Type by Route Segmentation and

Classification

Karol Waga, Andrei Tabarcea,Minjie Chen and Pasi Fränti

MOPSIPROJECTMOPSI

PROJECTUNIVERSITYOF EASTERN

FINLAND

UNIVERSITYOF EASTERN

FINLAND

University of Eastern Finland

JoensuuJoensuuJoki= a riverJoen = of a river

Suu = mouthJoensuu = mouth of a river

Motivation

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.

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 %

173 users 7,958 routes

5,208,205 points

Mopsi route collection4th October, 2012

Collected GPS routePlot on map

What is the activity?

Sp

eed

(km

/h)

Time

14

12

10

8

6

4

2

Collected GPS routeTime-vs-speed

0 1000 2000 3000 4000 5000 60000

2

4

6

8

10

12

14

time

spee

destimated segment result

Collected GPS routeGround truth

0 200 400 600 800 10000

5

10

15

20

25

time

spee

destimated segment result

Collected GPS routeAnother 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 contextneeded

Skiing

Speed? Track location, season

Train BusTime

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 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)

0 1 2 1arg min ( ( , ) ( )), 1...i nm D n i i t t i m

Number of segments

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

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

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)

Experiments

Segmentation of car route

Separating stop segments

Long distance running

Overall statisticsfrom running by move type

Interval training

Intervals

Warm-up &slow-down

Stops

Bicycle trip represented as carAlgorithm tries to be too clever

What next?

Further improvements

Boat Flight SkiingTrain 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