coarse indoor localization based on activity history ken le, avinash parnandi, pradeep vaghela,...
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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?
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2
Problem: GPS & Buildings ?
3 meters
Building
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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
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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
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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
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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
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7
IMU, Particle Filter, Detailed Map
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Previous Techniques Summary
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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
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Floor Level Localization
Accelerometer, no external infrastructure
Building map not required
Real-time
Simple yet useful, beyond GPS
Low Low Low YesAccelerometer
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Activity List for Floor Level Localization
11
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Data Collection and Analysis
HardwareHTC G1 Smartphone w/ Google Android OS
(embedded Accelerometer)
SoftwareAccelerometer Data Logger
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Data Collection and AnalysisA
ccel
erat
ion
Y
Samples
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Feature Based Classification
Misclassification Rate
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Feature Based Classification
walk
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Feature Based Classification
stairsup
stairsdown
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Experimentation
Feature Extractor UnlabeledActivityLogger
Feature Selector
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Experimentation
Training Activity Classification using Naive Bayes Classifier
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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
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Experiment Results
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Elevator Detection
Samples
Acc
eler
atio
n Y
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Elevator Detection
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Implementation
Main Screen State MachineRuns ubiquitously in background
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Implementation
Activity Sequence
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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
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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
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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
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Future Work
Evaluate various methods of predicting floor
level given the activity history
Develop framework for floor level localization
Phone location independence
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References
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[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/.
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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
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Questions?
www-scf.usc.edu/~hienle/fgl-gps-acc