Coarse Indoor Localization Based on Activity History Ken Le, Avinash Parnandi, Pradeep Vaghela, Aalaya Kolli, Karthik Dant...
Problem: GPS & Buildings ?
Sensor Networks
Fingerprinting with WiFi or GSM Location 1 Fingerprint A: Strong B: Moderate C: Weak WiFi AP WiFi AP WiFi AP
Fingerprinting with WiFi or GSM Location 2 Fingerprint A: Moderate B: Strong C: Moderate WiFi AP WiFi AP WiFi AP
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
Previous Techniques Summary
Indoor Localization with Activity History Floor Level Localization
Floor Level Localization <ul><li>Accelerometer, no external infrastructure </li></ul><ul><li>Building map not required </l...
Activity List for Floor Level Localization
Data Collection and Analysis Hardware HTC G1 Smartphone  w/ Google Android OS (embedded Accelerometer) Software Accelerome...
Data Collection and Analysis Acceleration Y Samples
Feature Based Classification Misclassification Rate
Feature Based Classification walk
Feature Based Classification stairs up stairs down
Experimentation Feature Extractor Unlabeled Activity Logger Feature Selector
Experimentation Training Activity Classification  using Naive Bayes Classifier
Dynamic Time Warping
Experiment Results
Elevator Detection Samples Acceleration Y
Elevator Detection
Implementation Main Screen State Machine Runs ubiquitously in background
Implementation Activity Sequence
Observations: Floor Localization - Walk-Stairs-Walk Sequences = One Floor Transition - (Elevator Ride Duration)/(Duration ...
Observations: Floor Localization - Walk-Stairs-Walk Sequences = X Floor Transition - ( Stairs Duration )/( Duration per Fl...
Conclusion <ul><li>Propose different technique for indoor localization </li></ul><ul><ul><li>infer coarse location (floor ...
Future Work <ul><li>Evaluate various methods of predicting floor level given the activity history </li></ul><ul><li>Develo...
References <ul><li>[1] Google Android. http://www.android.com </li></ul><ul><li>[2] L. Aalto, N. Gothlin, J. Korhonen, and...
References <ul><li>[10] E. Miluzzo, N. D. Lane, K. Fodor, R. Peterson, H. Lu, M. Musolesi,S. B. Eisenman, X. Zheng, and A....
Questions? www-scf.usc.edu/~hienle/fgl-gps-acc
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  1. 1. Coarse Indoor Localization Based on Activity History Ken Le, Avinash Parnandi, Pradeep Vaghela, Aalaya Kolli, Karthik Dantu, Sameera Poduri, Prof. Gaurav Sukhatme
  2. 2. Problem: GPS & Buildings ?
  3. 3. Sensor Networks
  4. 4. Fingerprinting with WiFi or GSM Location 1 Fingerprint A: Strong B: Moderate C: Weak WiFi AP WiFi AP WiFi AP
  5. 5. Fingerprinting with WiFi or GSM Location 2 Fingerprint A: Moderate B: Strong C: Moderate WiFi AP WiFi AP WiFi AP
  6. 6. Fingerprinting with WiFi or GSM Location 3 Fingerprint A: Weak B: Medium C: Strong WiFi AP WiFi AP WiFi AP
  7. 7. IMU, Particle Filter, Detailed Map
  8. 8. Previous Techniques Summary
  9. 9. Indoor Localization with Activity History Floor Level Localization
  10. 10. Floor Level Localization <ul><li>Accelerometer, no external infrastructure </li></ul><ul><li>Building map not required </li></ul><ul><li>Real-time </li></ul><ul><li>Simple yet useful, beyond GPS </li></ul>Low Low Low Yes Accelerometer
  11. 11. Activity List for Floor Level Localization
  12. 12. Data Collection and Analysis Hardware HTC G1 Smartphone w/ Google Android OS (embedded Accelerometer) Software Accelerometer Data Logger
  13. 13. Data Collection and Analysis Acceleration Y Samples
  14. 14. Feature Based Classification Misclassification Rate
  15. 15. Feature Based Classification walk
  16. 16. Feature Based Classification stairs up stairs down
  17. 17. Experimentation Feature Extractor Unlabeled Activity Logger Feature Selector
  18. 18. Experimentation Training Activity Classification using Naive Bayes Classifier
  19. 19. Dynamic Time Warping
  20. 20. Experiment Results
  21. 21. Elevator Detection Samples Acceleration Y
  22. 22. Elevator Detection
  23. 23. Implementation Main Screen State Machine Runs ubiquitously in background
  24. 24. Implementation Activity Sequence
  25. 25. Observations: Floor Localization - Walk-Stairs-Walk Sequences = One Floor Transition - (Elevator Ride Duration)/(Duration per floor) = # of Floor Transitions
  26. 26. Observations: Floor Localization - Walk-Stairs-Walk Sequences = X Floor Transition - ( Stairs Duration )/( Duration per Floor w/ Stairs ) ≈ # of Floor Transitions
  27. 27. Conclusion <ul><li>Propose different technique for indoor localization </li></ul><ul><ul><li>infer coarse location (floor level) based on user activities </li></ul></ul><ul><li>Simple yet useful information </li></ul><ul><ul><li>floor level </li></ul></ul><ul><li>Low equipment, installation, configuration </li></ul><ul><ul><li>practical for anyone </li></ul></ul>
  28. 28. Future Work <ul><li>Evaluate various methods of predicting floor level given the activity history </li></ul><ul><li>Develop framework for floor level localization </li></ul><ul><li>Phone location independence </li></ul>
  29. 29. References <ul><li>[1] Google Android. http://www.android.com </li></ul><ul><li>[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. </li></ul><ul><li>[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. </li></ul><ul><li>[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. </li></ul><ul><li>[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. </li></ul><ul><li>[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. </li></ul><ul><li>[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. </li></ul><ul><li>[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. </li></ul><ul><li>[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/. </li></ul>
  30. 30. References <ul><li>[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. </li></ul><ul><li>[11] T. M. Mitchell. Machine Learning. McGraw-Hill, New York, 1997. </li></ul><ul><li>[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. </li></ul><ul><li>[13] V. Otsason, A. Varshavsky, A. LaMarca, and E. de Lara. Accurate gsm indoor localization. pages 141 – 58, Berlin, Germany, 2005//. </li></ul><ul><li>[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/. </li></ul><ul><li>[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. </li></ul><ul><li>[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. </li></ul><ul><li>[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. </li></ul><ul><li>[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. </li></ul><ul><li>[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. </li></ul><ul><li>[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 </li></ul>
  31. 31. Questions? www-scf.usc.edu/~hienle/fgl-gps-acc

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