Coarse indoor localization based on activity history
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Coarse Indoor Localization Based on Activity History. Ken Le, Avinash Parnandi, Pradeep Vaghela, Aalaya Kolli, Karthik Dantu, Sameera Poduri, Prof. Gaurav Sukhatme. Problem: GPS & Buildings ?. Sensor Networks. Fingerprinting with WiFi or GSM. Location 1 Fingerprint A: Strong B: Moderate

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Coarse Indoor Localization Based on Activity History

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Coarse indoor localization based on activity history

Coarse Indoor Localization Based on Activity History

Ken Le, Avinash Parnandi, Pradeep Vaghela, Aalaya Kolli, Karthik Dantu, Sameera Poduri, Prof. Gaurav Sukhatme


Problem gps buildings

Problem: GPS & Buildings ?


Sensor networks

Sensor Networks


Fingerprinting with wifi or gsm

Fingerprinting with WiFi or GSM

Location 1 Fingerprint

A: Strong

B: Moderate

C: Weak

WiFi AP

WiFi AP

WiFi AP


Fingerprinting with wifi or gsm1

Fingerprinting with WiFi or GSM

Location 2 Fingerprint

A: Moderate

B: Strong

C: Moderate

WiFi AP

WiFi AP

WiFi AP


Fingerprinting with wifi or gsm2

Fingerprinting with WiFi or GSM

Location 3 Fingerprint

A: Weak

B: Medium

C: Strong

WiFi AP

WiFi AP

WiFi AP


Imu particle filter detailed map

IMU, Particle Filter, Detailed Map


Previous techniques summary

Previous Techniques Summary


Coarse indoor localization based on activity history

Indoor Localization with Activity History

Floor Level Localization


Floor level localization

Floor Level Localization

Accelerometer, no external infrastructure

Building map not required

Real-time

Simple yet useful, beyond GPS

Accelerometer

Low

Low

Low

Yes


Activity list for floor level localization

Activity List for Floor Level Localization

11


Data collection and analysis

Data Collection and Analysis

Hardware

HTC G1 Smartphone

w/ Google Android OS

(embedded Accelerometer)

Software

Accelerometer Data Logger


Data collection and analysis1

Data Collection and Analysis

Acceleration Y

Samples


Feature based classification

Feature Based Classification

Misclassification Rate


Feature based classification1

Feature Based Classification

walk


Feature based classification2

Feature Based Classification

stairs

down

stairs

up


Experimentation

Experimentation

Unlabeled

Activity

Logger

Feature Selector

Feature Extractor


Experimentation1

Experimentation

Activity Classification

using Naive Bayes Classifier

Training


Dynamic time warping

Dynamic Time Warping


Experiment results

Experiment Results


Elevator detection

Elevator Detection

Acceleration Y

Samples


Elevator detection1

Elevator Detection


Implementation

Implementation

State Machine

Runs ubiquitously in background

Main Screen


Implementation1

Implementation

Activity Sequence


Observations floor localization

Observations: Floor Localization

- Walk-Stairs-Walk Sequences = One Floor Transition

- (Elevator Ride Duration)/(Duration per floor) = # of Floor Transitions


Observations floor localization1

Observations: Floor Localization

- Walk-Stairs-Walk Sequences = X Floor Transition

- (Stairs Duration)/(Duration per Floor w/ Stairs) ≈ # of Floor Transitions


Conclusion

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


Future work

Future Work

Evaluate various methods of predicting floor level given the activity history

Develop framework for floor level localization

Phone location independence


References

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/.


References1

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

Questions?

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


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