I sensed he went up the stairs and walked for a bit

Coarse Indoor Localization Based on Activity HistoryKen Le, Avinash Parnandi, Pradeep Vaghela, Aalaya Kolli, Karthik

Dantu, Sameera Poduri, Prof. Gaurav Sukhatme

Last time I checked he was at 34.020283, 118.28903 +/- 3m.But then he entered a building,

you know how I am with buildings...

Regular GPS Receiver

Have you seen Bob?

2

Problem: GPS & Buildings ?

3 meters

Building

3

Sensor Networks

Up

93'-6 13/16"

1 2

6 87

43 5

9

Infrared SensorBluetooth Sensor

Ultrasound Beacon

Infrared EmitterBluetooth Device

Ultrasound Receiver

4

Fingerprinting with WiFi or GSM

Up

93'-6 13/16"

A

B

C

Location 1 FingerprintA: StrongB: ModerateC: Weak

WiFi AP

WiFi AP

WiFi AP

5

Fingerprinting with WiFi or GSM

Up

93'-6 13/16"

A

B

C

Location 2 FingerprintA: ModerateB: StrongC: Moderate

WiFi AP

WiFi AP

WiFi AP

6

Fingerprinting with WiFi or GSM

Up

93'-6 13/16"

A

B

C

Location 3 FingerprintA: WeakB: MediumC: Strong

WiFi AP

WiFi AP

WiFi AP

7

IMU, Particle Filter, Detailed Map

8

Previous Techniques Summary

9

34'-9

3/4

"

64'-3"

28'-6

1/1

6"

54'-5 7/8"

Z

walk1:00:10PM

1:00:20PMstairs up

1:00:45PMwalk

1:01:00PMstill

elevator up1:00:17PM

Indoor Localization with Activity History

Floor Level Localization

10

Floor Level Localization

Accelerometer, no external infrastructure

Building map not required

Real-time

Simple yet useful, beyond GPS

Low Low Low YesAccelerometer

Activity List for Floor Level Localization

11

12

Data Collection and Analysis

HardwareHTC G1 Smartphone w/ Google Android OS

(embedded Accelerometer)

SoftwareAccelerometer Data Logger

13

Data Collection and AnalysisA

ccel

erat

ion

Y

Samples

14

Feature Based Classification

Misclassification Rate

15

Feature Based Classification

walk

16

Feature Based Classification

stairsup

stairsdown

17

Experimentation

Feature Extractor UnlabeledActivityLogger

Feature Selector

18

Experimentation

Training Activity Classification using Naive Bayes Classifier

19

Dynamic Time Warping

Time Time Time

Acc

eler

atio

n Y

Stairs Up Walk Stairs Down

Acc

eler

atio

n Y

Acc

eler

atio

n Y

20

Experiment Results

21

Elevator Detection

Samples

Acc

eler

atio

n Y

22

Elevator Detection

23

Implementation

Main Screen State MachineRuns ubiquitously in background

24

Implementation

Activity Sequence

25

Observations: Floor Localization

- Walk-Stairs-Walk Sequences = One Floor Transition- (Elevator Ride Duration)/(Duration per floor) = # of Floor Transitions

X

Building Style 1

1st floor

2nd floor

3rd floor

4th floor

26

Observations: Floor Localization

- Walk-Stairs-Walk Sequences = X Floor Transition- (Stairs Duration)/(Duration per Floor w/ Stairs) ≈ # of Floor Transitions

X

Building Style 2

1st floor

2nd floor

3rd floor

4th floor

27

Conclusion

Propose different technique for indoor

localization

• infer coarse location (floor level) based on user

activities

Simple yet useful information

• floor level

Low equipment, installation, configuration

• practical for anyone

28

Future Work

Evaluate various methods of predicting floor

level given the activity history

Develop framework for floor level localization

Phone location independence

References

[1] Google Android. http://www.android.com

[2] L. Aalto, N. Gothlin, J. Korhonen, and T. Ojala. Bluetooth and wap push based location-aware mobile advertising system. In MobiSys ’04: Proceedings of the 2nd international conference on Mobile systems, applications, and services, pages 49–58, New York, NY, USA, 2004.ACM.

[3] J. Baek, G. Lee, W. Park, and B.-J. Yun. Accelerometer signal processing for user activity detection. volume Vol.3, pages 610 – 17, Berlin, Germany, 2004.

[4] P. Bahl and V. N. Padmanabhan. RADAR: An in-building RF-based user location and tracking system. In International Conference on Computer Communications (INFOCOM), pages 775–784, 2000.

[5] T. Choudhury, G. Borriello, S. Consolvo, D. Haehnel, B. Harrison, B. Hemingway, J. Hightower, P. . Klasnja, K. Koscher, A. Lamarca, J. A. Landay, L. Legrand, J. Lester, A. Rahimi, A. Rea, and D. Wyatt. The mobile sensing platform: An embedded activity recognition system. IEEE Pervasive Computing, 7(2):32–41, 2008.

[6] A. Jeon, J. Kim, I. Kim, J. Jung, S. Ye, J. Ro, S. Yoon, J. Son, B. Kim, B. Shin, and G. Jeon. Implementation of the personal emergency response system using a 3-axial accelerometer. In Information Technology Applications in Biomedicine, 2007. ITAB 2007. 6th International Special Topic Conference onX, pages 223–226, Nov. 2007.

[7] A. Jeon, J. Kim, I. Kim, J. Jung, S. Ye, J. Ro, S. Yoon, J. Son, B. Kim,B. Shin, and G. Jeon. Implementation of the personal emergency response system using a 3-axial accelerometer. pages 223 – 226,Tokyo, Japan, 2008.

[8] A. Krause, M. Ihmig, E. Rankin, D. Leong, S. Gupta, D. Siewiorek,A. Smailagic, M. Deisher, and U. Sengupta. Trading off prediction accuracy and power consumption for context-aware wearable computing. In ISWC ’05: Proceedings of the Ninth IEEE International Symposium on Wearable Computers, pages 20–26, Washington, DC, USA, 2005. IEEE Computer Society.

[9] M. Mathie, A. Coster, N. Lovell, and B. Celler. Accelerometry:providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiological Measurement, 25(2):1– 20, 2004/04/.

References

[10] E. Miluzzo, N. D. Lane, K. Fodor, R. Peterson, H. Lu, M. Musolesi,S. B. Eisenman, X. Zheng, and A. T. Campbell. Sensing meets mobile social networks: the design, implementation and evaluation of the cenceme application. In SenSys ’08: Proceedings of the 6th ACM conference on Embedded network sensor systems, pages 337–350, New York, NY, USA, 2008. ACM.

[11] T. M. Mitchell. Machine Learning. McGraw-Hill, New York, 1997.

[12] R. Muscillo, S. Conforto, M. Schmid, P. Caselli, and T. D’Alessio.Classification of motor activities through derivative dynamic time warping applied on accelerometer data. pages 4930–4933, Aug. 2007.

[13] V. Otsason, A. Varshavsky, A. LaMarca, and E. de Lara. Accurate gsm indoor localization. pages 141 – 58, Berlin, Germany, 2005//.

[14] S. Preece, J. Goulermas, L. Kenney, D. Howard, K. Meijer, and R. Crompton. Activity identification using body-mounted sensors-a review of classification techniques. Physiological Measurement, 30(4):R1–R33 –, 2009/04/.

[15] N. Ravi, N. Dandekar, P. Mysore, and M. L. Littman. Activity recognition from accelerometer data. volume 3, pages 1541 – 1546, Pittsburgh, PA, United states, 2005.

[16] A. Savvides, C.-C. Han, and M. B. Srivastava. Dynamic fine-grained localization in ad-hoc networks of sensors. In International Conference on Mobile Computing and Networking (MOBICOM), pages 166–179, 2001.

[17] A. Varshavsky, E. de Lara, J. Hightower, A. LaMarca, and V. Otsason.GSM indoor localization. Pervasive and Mobile Computing, 3(6):698–720, 2007.

[18] R. Want, A. Hopper, V. Falcao, and J. Gibbons. The active badge location system. ACM Transactions on Information Systems, 10(1):91– 102, Jan. 1992.

[19] A. Ward, A. Jones, and A. Hopper. A new location technique for the active office. Personal Communications, IEEE, 4(5):42–47, Oct 1997.

[20] O. Woodman and R. Harle. Pedestrian localisation for indoor environments. In UbiComp ’08: Proceedings of the 10th international conference on Ubiquitous computing, pages 114–123, New York, NY, USA, 2008. ACM

Questions?

www-scf.usc.edu/~hienle/fgl-gps-acc