Personal Identification Based on Radio Signal Strength for Ubiquitous Healthcare Systems

  • Lee, Jong-Shill (Department of Biomedical Engineering, Hanyang University) ;
  • Park, Sang-Hae (Research Development Team, GS FuelCell Co. Ltd.) ;
  • Chee, Young-Joon (Department of Biomedical Engineering, Hanyang University) ;
  • Kim, In-Young (Department of Biomedical Engineering, Hanyang University) ;
  • Kim, Sun-I. (Department of Biomedical Engineering, Hanyang University)
  • Published : 2007.06.30

Abstract

Personal identification is essential for the automatic measurement of biosignal information in home healthcare systems. Personal identification is usually achieved with passive radio frequency identification (RFID), which does little more than store a unique identification number. However, passive RFID is not ideal for automatic identification. We present a user identification system based on radio signal strength indication (RSSI) using ZigBee for active RFID tags. Personal identification is achieved by finding the largest RSSI value from aggregated beacon messages that are periodically transmitted by active RFID tags carried by users. Obtaining reliable person!'.! identification without restricting the orientation requires a certain distance between the closest active RFID tag from the ZED and the second closest tag. The results show that the closest active RFID tag from the ZED and the second closest tag must be at least 70 cm apart to achieve reliable personal identification.

References

  1. I.Y. Kim, 'Need for Ubiquitous Healthcare Technology,' Magazine of the IEEK, vol. 32, no. 12, pp.19-28, 2005
  2. J. Seo, J. Choi, B. Choi, D.U. Jeong, and K. Park, 'The development of a nonintrusive home based physiologic signal measurement system,' Telemedicine Journal and e-health, vol. 11, no. 4, pp.487-495, 2005 https://doi.org/10.1089/tmj.2005.11.487
  3. Lionel M. Ni, Yunhao Liu, Yiu Cho Lau, and Abhishek P. Patil, 'LANDMARC: Indoor Location Sensing Using Active RFID,' Wireless Networks, vol. 10, no. 6, Springer, pp.701-710, 2004 https://doi.org/10.1023/B:WINE.0000044029.06344.dd
  4. P. Bahl and V.N. Padmanabhan, 'RADAR: An in-building RF-based user location and tracking system,' in Proc. IEEE INFOCOM, 2000, pp.775-784
  5. M. Berna, B. Lisien, B. Shllner, G. G, F. Pfenning and S. Thrun, 'A learning algorithm for localizing people based on wireless signal strength that uses labeled and unlabeled data,' in Proc. IJCAI, pp.1427-1428, 2003
  6. E. Elnahrawy, X. Li and R.M. Martin, 'The limits of localizatior using strength: A comparative study,' in Proc. SECON, 2004
  7. D. Madigan, E. Elnahrawy and R. Martin, 'Bayesian indoor positioning systems,' in Proc. INFOCOM, vol. 2, pp.1217-1227 2005
  8. R. Stoleru and J. Stankovic, 'Probability grid:A locatior estimation scheme for wireless sensor networks,' in Proc. SECON, 2004
  9. K. Yedavalli, B. Krishnamachari, S. Ravula and B. Srinivasan 'Ecolocation: A technique for RF based localization in wireless sensor networks,' in Proc. IPSN, 2005
  10. http://www.chipcon.com/files/CC2420_Data_Sheet_l_3.pdf
  11. D. Lymberopouls and A. Savvides, 'xyz: A motion-enabled power aware sensor node platform for distributed sensor network applications,' In ISP, SPOTS track, 2005
  12. T.S. Rappaport, Wireless Communications Principles and Practice, Prentice Hall PTR, 1996