Abstract
Geographic location of a person is important contextual information that can be used in a variety of scenarios like disaster relief, directional assistance, context-based advertisements, etc. GPS provides accurate localization outdoors but is not useful inside buildings. We propose an coarse indoor localization approach that exploits the ubiquity of smart phones with embedded sensors. GPS is used to find the building in which the user is present. The Accelerometers are used to recognize the user’s dynamic activities (going up or down stairs or an elevator) to determine his/her location within the building. We demonstrate the ability to estimate the floor-level of a user. We compare two techniques for activity classification, one is naive Bayes classifier and the other is based on dynamic time warping. The design and implementation of a localization application on the HTC G1 platform running Google Android is also presented.
This work was supported in part by NSF grant CCR-0120778 (CENS: Center for Embedded Networked Sensing), and by a gift from the Okawa Foundation. It was initiated as a project for the graduate course CS 546: Intelligent Embedded Systems taught at USC in Spring 2009.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Google Android, http://www.android.com/
Aalto, L., Göthlin, N., Korhonen, J., Ojala, T.: Bluetooth and wap push based location-aware mobile advertising system. In: MobiSys 2004: Proceedings of the 2nd international conference on Mobile systems, applications, and services, pp. 49–58. ACM, New York (2004)
Baek, J., Lee, G., Park, W., Yun, B.-J.: Accelerometer signal processing for user activity detection, Berlin, Germany, vol. 3, pp. 610–617 (2004)
Bahl, P., Padmanabhan, V.N.: RADAR: An in-building RF-based user location and tracking system. In: International Conference on Computer Communications (INFOCOM), pp. 775–784 (2000)
Choudhury, T., Borriello, G., Consolvo, S., Haehnel, D., Harrison, B., Hemingway, B., Hightower, J., Klasnja, P., Koscher, K., Lamarca, A., Landay, J.A., Legrand, L., Lester, J., Rahimi, A., Rea, A., Wyatt, D.: The mobile sensing platform: An embedded activity recognition system. IEEE Pervasive Computing 7(2), 32–41 (2008)
Jeon, A., Kim, J., Kim, I., Jung, J., Ye, S., Ro, J., Yoon, S., Son, J., Kim, B., Shin, B., Jeon, G.: Implementation of the personal emergency response system using a 3-axial accelerometer. In: 6th International Special Topic Conference on Information Technology Applications in Biomedicine, ITAB 2007, vol. X, pp. 223–226 (November 2007)
Jeon, A., Kim, J., Kim, I., Jung, J., Ye, S., Ro, J., Yoon, S., Son, J., Kim, B., Shin, B., Jeon, G.: Implementation of the personal emergency response system using a 3-axial accelerometer, Tokyo, Japan, pp. 223–226 (2008)
Krause, A., Ihmig, M., Rankin, E., Leong, D., Gupta, S., Siewiorek, D., Smailagic, A., Deisher, M., Sengupta, U.: Trading off prediction accuracy and power consumption for context-aware wearable computing. In: ISWC 2005: Proceedings of the Ninth IEEE International Symposium on Wearable Computers, Washington, DC, USA, pp. 20–26. IEEE Computer Society, Los Alamitos (2005)
Mathie, M., Coster, A., Lovell, N., Celler, B.: Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiological Measurement 25(2), 1–20 (2004)
Miluzzo, E., Lane, N.D., Fodor, K., Peterson, R., Lu, H., Musolesi, M., Eisenman, S.B., Zheng, X., Campbell, A.T.: Sensing meets mobile social networks: the design, implementation and evaluation of the cenceme application. In: SenSys 2008: Proceedings of the 6th ACM conference on Embedded network sensor systems, pp. 337–350. ACM, New York (2008)
Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)
Muscillo, R., Conforto, S., Schmid, M., Caselli, P., D’Alessio, T.: Classification of motor activities through derivative dynamic time warping applied on accelerometer data, August 2007, pp. 4930–4933 (2007)
Otsason, V., Varshavsky, A., LaMarca, A., de Lara, E.: Accurate gsm indoor localization, Berlin, Germany, pp. 141–58 (2005)
Preece, S., Goulermas, J., Kenney, L., Howard, D., Meijer, K., Crompton, R.: Activity identification using body-mounted sensors-a review of classification techniques. Physiological Measurement 30(4), R1–R33 (2009)
Ravi, N., Dandekar, N., Mysore, P., Littman, M.L.: Activity recognition from accelerometer data, Pittsburgh, PA, United states, vol. 3, pp. 1541–1546 (2005)
Savvides, A., Han, C.-C., Srivastava, M.B.: Dynamic fine-grained localization in ad-hoc networks of sensors. In: International Conference on Mobile Computing and Networking (MOBICOM), pp. 166–179 (2001)
Varshavsky, A., de Lara, E., Hightower, J., LaMarca, A., Otsason, V.: GSM indoor localization. Pervasive and Mobile Computing 3(6), 698–720 (2007)
Want, R., Hopper, A., Falcao, V., Gibbons, J.: The active badge location system. ACM Transactions on Information Systems 10(1), 91–102 (1992)
Ward, A., Jones, A., Hopper, A.: A new location technique for the active office. IEEE Personal Communications 4(5), 42–47 (1997)
Woodman, O., Harle, R.: Pedestrian localisation for indoor environments. In: UbiComp 2008: Proceedings of the 10th international conference on Ubiquitous computing, pp. 114–123. ACM, New York (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Parnandi, A. et al. (2010). Coarse In-Building Localization with Smartphones. In: Phan, T., Montanari, R., Zerfos, P. (eds) Mobile Computing, Applications, and Services. MobiCASE 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 35. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12607-9_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-12607-9_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12606-2
Online ISBN: 978-3-642-12607-9
eBook Packages: Computer ScienceComputer Science (R0)