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Coarse Indoor Localization Based on Activity History

This paper proposes a simple yet useful technique for indoor localization based on user activities. By analyzing activity history and using a combination of sensors, such as accelerometer, WiFi, and GSM, the system can infer the user's floor level without the need for complex infrastructure or building maps. The technique is low-cost and practical for anyone to implement.

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Coarse Indoor Localization Based on Activity History

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  1. Coarse Indoor Localization Based on Activity History Ken Le, Avinash Parnandi, Pradeep Vaghela, Aalaya Kolli, Karthik Dantu, Sameera Poduri, Prof. Gaurav Sukhatme

  2. Problem: GPS & Buildings ?

  3. Sensor Networks

  4. Fingerprinting with WiFi or GSM Location 1 Fingerprint A: Strong B: Moderate C: Weak WiFi AP WiFi AP WiFi AP

  5. Fingerprinting with WiFi or GSM Location 2 Fingerprint A: Moderate B: Strong C: Moderate WiFi AP WiFi AP WiFi AP

  6. Fingerprinting with WiFi or GSM Location 3 Fingerprint A: Weak B: Medium C: Strong WiFi AP WiFi AP WiFi AP

  7. IMU, Particle Filter, Detailed Map

  8. Previous Techniques Summary

  9. 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 Accelerometer Low Low Low Yes

  11. Activity List for Floor Level Localization 11

  12. Data Collection and Analysis Hardware HTC G1 Smartphone w/ Google Android OS (embedded Accelerometer) Software Accelerometer Data Logger

  13. Data Collection and Analysis Acceleration Y Samples

  14. Feature Based Classification Misclassification Rate

  15. Feature Based Classification walk

  16. Feature Based Classification stairs down stairs up

  17. Experimentation Unlabeled Activity Logger Feature Selector Feature Extractor

  18. Experimentation Activity Classification using Naive Bayes Classifier Training

  19. Dynamic Time Warping

  20. Experiment Results

  21. Elevator Detection Acceleration Y Samples

  22. Elevator Detection

  23. Implementation State Machine Runs ubiquitously in background Main Screen

  24. Implementation Activity Sequence

  25. Observations: Floor Localization - Walk-Stairs-Walk Sequences = One Floor Transition - (Elevator Ride Duration)/(Duration per floor) = # of Floor Transitions

  26. Observations: Floor Localization - Walk-Stairs-Walk Sequences = X Floor Transition - (Stairs Duration)/(Duration per Floor w/ Stairs) ≈ # of Floor Transitions

  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

  29. 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/.

  30. 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

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

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