[Paper Report] Mobile Network and Applications
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Transcript of [Paper Report] Mobile Network and Applications
University of Helsinki, Finland Department of Computer Science
Accelerometer-Based Transportation Mode Detection on Smartphones
Professor: Ren-Shiou Liu Student: Yuan-Chung Hou
Department of Industrial and Information Management
University of Helsinki, Finland Department of Computer Science
Accelerometer-Based Transportation Mode Detection on Smartphones
Professor: Ren-Shiou Liu Student: Yuan-Chung Hou
Department of Industrial and Information Management
What is accelerometer? !2
What is accelerometer? !2
What is accelerometer? !2
Purpose
!4
Purpose
!4
Purpose
!4
Purpose
!4
Purpose
!4
Purpose
!4
Purpose!6
Purpose!6
GPS?
Purpose!6
GPS?
Purpose!6
GPS?high power consumption
Purpose!6
GPS?high power consumption
depend on signal
Purpose!6
GPS?high power consumption
depend on signal
inaccuracy
1. Wang
2. Reddy
3. Jigsaw
Other technique!7
1. Wang
2. Reddy
3. Jigsaw
Other technique!7
improve 20%
Process!8
Process!8
Gravity estimation
Feature Extraction
Gravity Estimation
1. limited situation
2. unskillful
Other method Paper method 1. more accuracy
2. solve unstable situation
!9
Gravity Estimation
1. limited situation
2. unskillful
Other method Paper method 1. more accuracy
2. solve unstable situation
!9
Gravity Estimation
1. limited situation
2. unskillful
Other method Paper method 1. more accuracy
2. solve unstable situation
!9
Gravity Estimation
1. limited situation
2. unskillful
Other method Paper method 1. more accuracy
2. solve unstable situation
!9
Gravity Estimation
1. limited situation
2. unskillful
Other method Paper method 1. more accuracy
2. solve unstable situation
!9
Gravity Estimation
1. limited situation
2. unskillful
Other method Paper method 1. more accuracy
2. solve unstable situation
Too close!!!
!9
Feature Extraction!10
capture high frequency motion
Feature Extraction
Frame-based features
!10
capture high frequency motion
Feature Extraction
Frame-based features
!10
Peak-based features1. capture low frequency motion
2. distinguish different motorized transportation
capture high frequency motion
Feature Extraction
Frame-based features
!10
Peak-based features1. capture low frequency motion
2. distinguish different motorized transportation
Segment-based featuresdetect acceleration and deceleration
1st Difficulty!11
1st Difficulty!11
Process!12
Gravity estimation
Feature Extraction
Process!12
Gravity estimation
Feature Extraction
Classification
improving the accuracy of any learning algorithm
Classification
Adaptive Boosting
!13
improving the accuracy of any learning algorithm
Classification
Adaptive Boosting
!13
Kinematic Motion Classifier
improving the accuracy of any learning algorithm
Classification
Adaptive Boosting
!13
Kinematic Motion Classifier
improving the accuracy of any learning algorithm
Classification
Adaptive Boosting
!13
Kinematic Motion Classifier
Motorized Classifier
improving the accuracy of any learning algorithm
Classification
Adaptive Boosting
!13
Kinematic Motion Classifier
Motorized Classifier
!14
Classification
Process!15
Gravity estimation
Feature Extraction
Classification
Process!15
Gravity estimation
Feature Extraction
Classification Performance
Performance!16
Accuracy
Performance!16
Accuracy
Performance!16
Accuracy
Performance
Influence of device placement
!17
results of cross-placement evaluation
Performance
Power Consumption
!18
Performance
Power Consumption
!18
Accelerometer saves
more power.
Generalization Performance !19
40% training data ; other as test data
Generalization Performance !19
40% training data ; other as test data
perform well at capturing generic data
Summary!20
Summary
1. more accuracy than other technique
!20
Summary
1. more accuracy than other technique
!20
2. conserve battery power
Application
source: http://goo.gl/CeEibz ; http://goo.gl/K5Ziv2 ;
!21
recognize potholes car accident
More can do!22
More can do!22
1. switch off the accelerometer depend on situation
More can do!22
1. switch off the accelerometer depend on situation
2. better when user interact or orientation change
More can do!22
1. switch off the accelerometer depend on situation
2. better when user interact or orientation change
3. better the mode change detection
More can do!22
1. switch off the accelerometer depend on situation
2. better when user interact or orientation change
3. better the mode change detection
4. solve the metro and train detection
More can do!22
1. switch off the accelerometer depend on situation
2. better when user interact or orientation change
3. better the mode change detection
4. solve the metro and train detection
1. http://www.cs.helsinki.fi/u/shemmink/Transportation/hemminki13transportation.pdf
2. http://www.cis.fordham.edu/wisdm/includes/files/sensorKDD-2010.pdf
Reference!23
Thanks for listening