Using Mobile Phones to Determine Transportation Modes 2011.04.11 Hyeong-il Ko Sasank Reddy et al., ACM Transactions on Sensor Networks, Vol. 6, No. 2,

Using Mobile Phones to Determine Transportation Modes

2011.04.11

Hyeong-il Ko

Sasank Reddy et al.,

ACM Transactions on Sensor Networks, Vol. 6, No. 2, Article 13, Feb 2010

S FT COMPUTING @ YONSEI UNIV . KOREA

Contents

• Introduction

• Related Work

• Approach

• Experimental Setup

• Results

• Conclusion

2


Introduction

• Mobile phones– Computation, sensing, and communication capabilities– Carried by people throughout the day

• Target applications for transportation mode inference and loca-tion information

– Physical Activity Monitoring– Personal Impact and/or Exposure Monitoring– Transportation and Mobility-Based Recruitment

3


Related Work

4


Approach

• Design Goal– User convenience

• 4 properties of suggested system to be convenient– Contained in one sensing unit– Flexible in terms of the position and orientation– Able to work for a variety of users without additional training– Not reliant on external spatial or user history based indexes

• Contribution– Suggested classifier that uses information from an accelerometer

and a GPS– Able to run on a commodity mobile device

5


Approach

• Sensor Selection– Bluetooth

• Not ubiquitous in outdoor settings– Static Bluetooth beacons mainly exists indoor settings

• Difficult to distinguish if an individual is moving

• Difficult to distinguish if an environment is changing– Other people carrying devices are moving

6


Sensor Selection

– WiFi and GSM

• Not discriminative when speed profiles are similar– Slow moving traffic, biking, and walking

• Depends on the density network and end points

7


Sensor Selection

– Accelerometer and GPS• 10~20% accuracy dropped if only 1 sensor of them is used• Negligible 0.6% improved when 4 sensing modalities are used

8


Feature Selection

• Feature Selection– Window Size

• A window of 1 sec

– Type of Features• Accelerometer

– Magnitude of the force vector from 3 axises

– Mean, variance, energy, and DFT energy coefficient between 1-10Hz

• Speed– Value directly used from GPS receiver

• Noise filtering step– Discarding GPS points deemed invalid– Excluding accelerometer data if too few samples are received for classification

9


Feature Selection

• Selection Method– CFS(Correlation based Feature Selection)

• Feature subset selector that eliminates irrelevant and redundant attributes• Feature subset

– Variance along with DFT energy coefficients between 1-3Hz– Speed from the GPS receiver

10


Classifier Selection

• Classifiers– Instance Classifiers

• E.g. C4.5 DT, KMC, NB, NN, and SVM

– CHMM(Continuous HMM)• Output symbols : independent multi-variate Gaussian distributions• Hidden states : classification classes• Transition probability

– Instance based classifier + DHMM(Discrete Hidden Markov Model)• DHMM output symbols : instance-based classifications• Hidden states : classification classes• State transition probabilities

11


Experimental Setup

• Hardware Platform– Nokia n95

• CPU : 332 MHz ARM processor• RAM : 128MB• 3 axis accelerometer that can sample at 32 Hz• Built-in GPS receiver that can sample at 1 Hz• WiFi radio that can scan at 0.33 Hz• GSM cell radio that can sample at 1 Hz• Bluetooth radio that can scan at 0.08 Hz• Battery : 950 mAh• OS : Symbian S60 3rd Edition

• Software Setup– Weka Machine Learning Toolkit– Generalized Hidden Markov Model library– Python

12


Experimental Setup

• Data Collection– Volunteers to obtain data set

• 16 individuals– 8 male + 8 female– The ages of 20-45

– Accelerometer, GPS, WiFi, and GSM information obtained

– How to collect data• 1.25 hrs of data per position per individual

– Positions : Still, Walk, Run, Bike, Motor, All• Total 120 hrs

13


Results

• Classification Accuracy

14


Results

• Structure of Overall Classifier

15


Results

• Device Placement Variation– Mobile phones are often carried at different positions

– Classifier is trained on data from all 6 positions• Arm, bag, chest, hand, pocket, and waist

16


Results

• Extended Transportation Mode Traces– Would the DT+DHMM classifier perform in “everyday” use?

– 1 of the volunteers • carried the mobile phone over 4 weeks• documented instances of each of the transportation modes

17


Results

• Extended Transportation Mode Traces(Cont’d)– Would the DT+DHMM classifier perform in challenging urban envi-

ronment?

– The volunteer • Carried the mobile phone 3.5 hrs in urban canyons• At least 30 mins for each transportation mode

– Result• Average accuracy : 92.6%• Still, walking, and motorized transport : 95%• Biking and running state : around 88%

18


Results

• Memory and CPU Benchmarks– Using Nokia Energy Profiler

• 20 mins trials were performed

19


Results

• Energy Consumption

20


Results

• Energy-Aware Detection– Authors’ objective

• To create a transportation classifier that captures the behavior of individuals when they are outside

– The classifier should be energy efficient

– The most effective method to determine when the user is outdoors again

• GPS– Sampling GPS for the purpose is power hungry

– Trigger approach proposed• Attempting to sample the GPS when only changes occur to the primary GSM cell

tower would be more efficient in terms of energy usage• GSM cell towers are used to determine the start of outdoor trips• Filter to eliminate the “ping pong” effect

21


Results

– Trigger approach proposed(Cont’d)• To test the performance of the GSM triggered approach,

– 16 individuals labeled indoor/outdoor status and collected GSM cell tower every 1 sec for a day

• Total time of the day trace data collection– Average : 23.2 hrs– Mininum time : 20.7 hrs– Maximum time : 26.8 hrs

• Corresponding outdoor time– Average : 3.09 hrs– Maximum : 12.0 hrs– Minimum : 0.93 hrs

• The average percentage of outdoor time identified– 91.5%

• 12.4% energy save compared to GPS

22


Conclusion

• Transportation mode classification system– Distinguishing between being stationary, walking, running, biking

and in motorized travel– Employing a DT followed by a DHMM– Using a mobile phone equipped with a GPS receiver and an ac-

celerometer– Convenient for a user

• By not having strict position and orientation requirements

– Achieving a high accuracy level(93.6%)• Based on a dataset of 120 hrs of data from 16 users

– Not relying on external spatial indexes– Working well without user-specific training information

23

Using Mobile Phones to Determine Transportation Modes 2011.04.11 Hyeong-il Ko Sasank Reddy et al., ACM Transactions on Sensor Networks, Vol. 6, No. 2,

Documents

s ft computing

korea sensor selection

korea related work

gps able

instancebased classifications

sensor networks

mobilitybased recruitment

probability instance