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Qualitative Tracking of Objects in a Smart Home: A Passive RFID Approach Based on Decision Trees

Published: 29 June 2016 Publication History

Abstract

This paper presents a novel Indoor Tracking System (ITS) based on passive radio-frequency identification (RFID) technology. The new ITS exploits decision trees built from one dataset per room of a smart home. The datasets are built using a bottle equipped with four class 3 RFID tags and by dividing each room into qualitative zones. The paper discusses how to exploit positioning from decision trees to implement real-time tracking. The long term goal of this ITS is to extract qualitative spatial information to improve recognition of daily living activities' granularity. The results obtained are very encouraging as the average accuracy of the trajectories recognized is over 75%.

References

[1]
A. Bekkali, H. Sanson, and M. Matsumoto. Rfid indoor positioning based on probabilistic rfid map and kalman filtering. In Wireless and Mobile Computing, Networking and Communications, 2007. WiMOB 2007. Third IEEE International Conference on, pages 21--21. IEEE, 2007.
[2]
F. Bergeron, K. Bouchard, S. Gaboury, S. Giroux, and B. Bouchard. Indoor positioning system for smart homes based on decision trees and passive rfid. In Pacific Asia Knowledge Discovery and Data Mining Conference (PAKDD), 2016 Conference on. PAKDD, In press.
[3]
K. Bouchard, D. Fortin-Simard, S. Gaboury, B. Bouchard, and A. Bouzouane. Accurate trilateration for passive rfid localization in smart homes. International Journal of Wireless Information Networks, 21(1):32--47, 2013.
[4]
L. Chen, J. Hoey, C. D. Nugent, D. J. Cook, and Z. Yu. Sensor-based activity recognition. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 42(6):790--808, 2012.
[5]
L. Chen, C. D. Nugent, and H. Wang. A knowledge-driven approach to activity recognition in smart homes. Knowledge and Data Engineering, IEEE Transactions on, 24(6):961--974, 2012.
[6]
S. Chernbumroong, S. Cang, A. Atkins, and H. Yu. Elderly activities recognition and classification for applications in assisted living. Expert Systems with Applications, 40(5):1662--1674, 2013.
[7]
G. Demiris, B. K. Hensel, M. Skubic, and M. Rantz. Senior residentsâĂŹ perceived need of and preferences for âĂIJsmart homeâĂİ sensor technologies. International journal of technology assessment in health care, 24(01):120--124, 2008.
[8]
E. Frank Lopresti, A. Mihailidis, and N. Kirsch. Assistive technology for cognitive rehabilitation: State of the art. Neuropsychological rehabilitation, 14(1-2):5--39, 2004.
[9]
J. Han, C.-S. Choi, W.-K. Park, I. Lee, and S.-H. Kim. Smart home energy management system including renewable energy based on zigbee and plc. Consumer Electronics, IEEE Transactions on, 60(2):198--202, 2014.
[10]
X. Liu, J. Peng, and T. Liu. A novel indoor localization system based on passive rfid technology. In Electronic and Mechanical Engineering and Information Technology (EMEIT), 2011 International Conference on, pages 4285--4288. IEEE, 2011.
[11]
U. Nations. World population ageing 2013. Department of Economic and Social Affairs PD, 2013.
[12]
L. M. Ni, Y. Liu, Y. C. Lau, and A. P. Patil. Landmarc: indoor location sensing using active rfid. Wireless networks, 10(6):701--710, 2004.
[13]
P. Rashidi and D. J. Cook. Keeping the resident in the loop: Adapting the smart home to the user. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 39(5):949--959, 2009.
[14]
C. Roehrig, A. Heller, D. Hess, and F. Kuenemund. Global localization and position tracking of automatic guided vehicles using passive rfid technology. In ISR/Robotik 2014; 41st International Symposium on Robotics; Proceedings of, pages 1--8, June 2014.
[15]
A. M. Sisko, S. P. Keehan, G. A. Cuckler, A. J. Madison, S. D. Smith, C. J. Wolfe, D. A. Stone, J. M. Lizonitz, and J. A. Poisal. National health expenditure projections, 2013-23: faster growth expected with expanded coverage and improving economy. Health Affairs, 33(10):1841--1850, 2014.
[16]
L. Yang, J. Cao, W. Zhu, and S. Tang. A hybrid method for achieving high accuracy and efficiency in object tracking using passive rfid. In Pervasive Computing and Communications (PerCom), 2012 IEEE International Conference on, pages 109--115. IEEE, 2012.
[17]
J. Yim. Introducing a decision tree-based indoor positioning technique. Expert Systems with Applications, 34(2):1296--1302, 2008.

Cited By

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  • (2017)RFID based activities of daily living recognition2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)10.1109/UIC-ATC.2017.8397548(1-5)Online publication date: Aug-2017
  • (2017)BitID: Easily Add Battery-Free Wireless Sensors to Everyday Objects2017 IEEE International Conference on Smart Computing (SMARTCOMP)10.1109/SMARTCOMP.2017.7946990(1-8)Online publication date: May-2017
  • (2016)Real-Time Constraints for Activities of Daily Living RecognitionProceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments10.1145/2910674.2935840(1-2)Online publication date: 29-Jun-2016

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PETRA '16: Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments
June 2016
455 pages
ISBN:9781450343374
DOI:10.1145/2910674
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 June 2016

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Author Tags

  1. Decision Trees
  2. RFID
  3. Smart home
  4. Tracking

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Cited By

View all
  • (2017)RFID based activities of daily living recognition2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)10.1109/UIC-ATC.2017.8397548(1-5)Online publication date: Aug-2017
  • (2017)BitID: Easily Add Battery-Free Wireless Sensors to Everyday Objects2017 IEEE International Conference on Smart Computing (SMARTCOMP)10.1109/SMARTCOMP.2017.7946990(1-8)Online publication date: May-2017
  • (2016)Real-Time Constraints for Activities of Daily Living RecognitionProceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments10.1145/2910674.2935840(1-2)Online publication date: 29-Jun-2016

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