UNIVERSITY
OF EASTERN
FINLAND
MOPSI
PROJECT
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
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
Android
iPhone
None
Trends and popularity of GPS
Current situation
Nokia:
50% of its smart phones has GPS by 2010-12.
Gartner:
75% has GPS by the end of 2011.
70 %
Our lab:
Nokia
Android
iPhone
8
4
0
47 %
24 %
0%
None
5
30 %
Mopsi route collection
4th October, 2012
173 users
7,958 routes
5,208,205 points
Collected GPS route
Plot on map
Collected GPS route
Time-vs-speed
14
Speed (km/h)
12
10
8
6
4
2
Time
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:
Train
Boat
Spatial
context
needed
Flight
Skiing
Speed?
Bus
Time
tables
Track location,
season
Problems attacked
Problems addressed:
• Training material is not always available
• Problem of over-fit
• Loss of generalization
Limitations of current solution:
Rule-based!
• Correlation between neighboring segments
• Accuracy of segmentation
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:
f  
i j 1
ij
(ti j1  ti j )
j
Optimal segmentation:
D( s, r )  min( D( c, r  1)   cs (ts  tc )), c  (1...s  1)
A( s, r )  arg min c ( D( c, r  1)   cs (ts  tc ))
O(n2k)
Number of segments
m0  arg mini ( D(n, i )  1i  2 (tn  t1 )), i  1...m
Move type classification
A priori probabilities
1
Stop Walk
Car
0.8
Run
Probability
Bike
0.6
0.4
0.2
0
10
20
Speed(km/h)
30
40
2nd order Hidden Markov Model
Previous segment
Cost function:
Next segment
M
f   P(mi | Xi , mi 1 , mi 1 )
i 1
Cost function:
M
P(mi  2 | mi , mi 1 ) P(mi 1 | X )
f 
P(mi  2 )
i 1
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
Stops
Warm-up &
slow-down
Bicycle trip represented as car
Algorithm tries to be too clever
What next?
Further improvements
More move types
Train
Boat
Flight
Better stop detection
Generate ground truth
Skiing
Bus
New movement types
Skiing
Train
Flight
Conclusions
Method that (usually) works!
Simple to implement
No training data required