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

!3

source: http://goo.gl/uRJ4ei

What is accelerometer?

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