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Unified Fingerprinting/Ranging Localization for e-Healthcare Systems

  • Javier PrietoEmail author
  • Juan F. De Paz
  • Gabriel Villarrubia
  • Javier Bajo
  • Juan M. Corchado
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 376)

Abstract

Indoor localization constitutes one of the main pillars for the provision of context-aware services in e-Healthcare systems. Fingerprinting and ranging have traditionally been placed facing each other to meet the localization requirements. However, accurate fingerprinting may worth the exhaustive calibration effort in some critical areas while easy-to-deploy ranging can provide adequate accuracy for certain non-critical spaces. In this paper, we propose a framework and algorithm for seamless integration of both systems from the Bayesian perspective. We assessed the proposed framework with conventional WiFi devices in comparison to conventional implementations. The presented techniques exhibit a remarkable accuracy improvement while they avoid computationally exhaustive algorithms that impede real-time operation.

Keywords

Bayesian data fusion Fingerprinting Ranging RSS 

Notes

Acknowledgments

This work has been supported by the Spanish Government through the project iHAS (grant TIN2012-36586-C01/C02/C03) and FEDER funds.

References

  1. 1.
    Y. Bar-Shalom, X.R. Li, T. Kirubarajan, Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software (Wiley, New York, 2001)CrossRefGoogle Scholar
  2. 2.
    N. Van den Berg, M. Schumann, K. Kraft, W. Hoffmann, Telemedicine and telecare for older patients-a systematic review. Maturitas 73(2), 94–114 (2012)CrossRefGoogle Scholar
  3. 3.
    S. Bybordi, L. Reggiani, Hybrid fingerprinting-EKF based tracking schemes for indoor passive localization. Int. J. Distrib. Sens. Netw. 2014, 1–11 (2014)CrossRefGoogle Scholar
  4. 4.
    J.M. Corchado, J. Bajo, A. Abraham, GerAmi: improving healthcare delivery in geriatric residences. IEEE Intell. Syst. 23(2), 19–25 (2008)CrossRefGoogle Scholar
  5. 5.
    J.A. Fraile, Y. de Paz, J. Bajo, J.F. de Paz, B.P. Lancho, Context-aware multiagent system: planning home care tasks. Knowl. Inf. Syst. 40(1), 171–203 (2014)Google Scholar
  6. 6.
    F. Gustafsson, F. Gunnarsson, Mobile positioning using wireless networks. IEEE Signal Process. Mag. 22(4), 41–53 (2005)CrossRefGoogle Scholar
  7. 7.
    S. Julier, J. Uhlmann, H.F. Durrant-Whyte, A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Trans. Autom. Control 45(3), 477–482 (2000)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    M.H. Kabir, R. Kohno, A hybrid TOA-fingerprinting based localization of mobile nodes using UWB signaling for non line-of-sight conditions. Sensors 12(8), 11187–11204 (2012)CrossRefGoogle Scholar
  9. 9.
    J. Li, B. Zhang, H. Liu, L. Yu, Z. Wang, An indoor hybrid localization approach based on signal propagation model and fingerprinting. Int. J. Smart Home 7(6), 157–170 (2013)CrossRefGoogle Scholar
  10. 10.
    K.F. Li, Smart home technology for telemedicine and emergency management. J. Ambient Intell. Humanized Comput. 4(5), 535–546 (2013)CrossRefGoogle Scholar
  11. 11.
    J. Prieto, S. Mazuelas, A. Bahillo, P. Fernández, R.M. Lorenzo, E.J. Abril, Adaptive data fusion for wireless localization in harsh environments. IEEE Tran. Sig. Proc. 60(4), 1585–1596 (2012)CrossRefGoogle Scholar
  12. 12.
    G. Villarrubia, J.F. de Paz, J. Bajo, J.M. Corchado, Monitoring and detection platform to prevent anomalous situations in home care. Sensors 14(6), 9900–9921 (2014)CrossRefGoogle Scholar
  13. 13.
    F. Zampella, A. Bahillo, J. Prieto, A.R. Jiménez, F. Seco, Pedestrian navigation fusing inertial and RSS/TOF measurements with adaptive movement/measurement models: experimental evaluation and theoretical limits. Sens. Actuators A: Phys. 203, 249–260 (2013)CrossRefGoogle Scholar
  14. 14.
    C. Zato, G. Villarrubia, A. Sánchez, I. Barri, E. Rubión, A. Fernández, C. Rebate, J.A. Cabo, T. Álamos, J. Sanz, J. Seco, J. Bajo, J.M. Corchado, PANGEA—platform for automatic coNstruction of orGanizations of intElligent agents, in Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol. 151 (Springer, Berlin, 2012), pp. 229–239Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Javier Prieto
    • 1
    Email author
  • Juan F. De Paz
    • 1
  • Gabriel Villarrubia
    • 1
  • Javier Bajo
    • 2
  • Juan M. Corchado
    • 1
  1. 1.BISITE Research GroupUniversity of SalamancaSalamancaSpain
  2. 2.Departamento de Inteligencia Artificial, Facultad de InformáticaUniversidad Politécnica de MadridMadridSpain

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