Skip to main content
Springer Nature Link
Log in

Person Identification by Analyzing Door Accelerations in Time and Frequency Domain

  • Conference paper
  • First Online:
Ambient Intelligence (AmI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9425))

Included in the following conference series:

Abstract

The paper describes an approach for recognizing a person entering a room using only door accelerations. The approach analyzes the acceleration signal in time and frequency domain. For each domain two types of methods were developed: (i) feature-based – use features to describe the acceleration and then uses classification method to identify the person; (ii) signal-based – use the acceleration signal as input and finds the most similar ones in order to identify the person. The four methods were evaluated on a dataset of 1005 entrances recorded by 12 people. The results show that the time-domain methods achieve significantly higher accuracy compared to the frequency-domain methods, with signal-based method achieving 86 % accuracy. Additionally, the four methods were combined and all 15 combinations were examined. The best performing combined method increased the accuracy to 90 %. The results confirm that it is possible to identify a person entering a room using the door’s acceleration.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Aarts, E.: Ambient intelligence: a multimedia perspective. IEEE Multimedia 11(1), 12–19 (2004)

    Article  Google Scholar 

  2. Cook, D.J., Augusto, J.C., Jakkula, V.R.: Ambient intelligence: technologies, applications, and opportunities. Pervasive Mobile Comput. 5(4), 277–298 (2009)

    Article  Google Scholar 

  3. Bashir, K., Xiang, T., Gong, S.: Gait recognition without subject cooperation. Pattern Recogn. Lett. 31, 2052–2060 (2010)

    Article  Google Scholar 

  4. Wang, L., Tan, T., Ning, H., Hu, W.: Silhouette analysis-based gait recognition for human identification. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1505–1518 (2003)

    Article  Google Scholar 

  5. Gjoreski, H., Gams, M., Luštrek, M.: Context-based fall detection and activity recognition using inertial and location sensors. J. Ambient Intell. Smart Environ. 6(4), 419–433 (2014)

    Google Scholar 

  6. Li, Q., Stankovic, J., Hanson, M., Barth, A.T., Lach, J., Zhou, G.: Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. In: Proceedings of Sixth International Workshop on Wearable and Implantable Body Sensor Networks, pp. 138–143 (2009)

    Google Scholar 

  7. Gjoreski, H., Kozina, S., Gams, M., Luštrek, M., Álvarez-García, J.A., Hong, J.H., Ramos, J., Dey, A.K., Bocca, M., Patwari, N.: Competitive live evaluation of activity-recognition systems. IEEE Pervasive Comput. 14(1), 70–77 (2015)

    Article  Google Scholar 

  8. Gjoreski, H., Kozina, S., Luštrek, M., Gams, M.: Using multiple contexts to distinguish standing from sitting with a single accelerometer. In: European Conference on Artificial Intelligence (ECAI) (2014)

    Google Scholar 

  9. Vyas, N., Farringdon, J., Andre, D., Stivoric, J.: Machine learning and sensor fusion for estimating continuous energy expenditure. In: Proceedings of 23rd Conference on Innovative Applications of Artificial Intelligence, IAAI, 2011, pp. 1613–1620, San Francisco, CA, USA (2011)

    Google Scholar 

  10. Gjoreski, H., Kaluža, B., Gams, M., Milić, R., Luštrek, M.: Context-based ensemble method for human energy expenditure estimation. Applied Soft Computing, (2015) (in press)

    Google Scholar 

  11. Patent application, door monitoring system, patent no. WO 2011011282 A2

    Google Scholar 

  12. Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. J. Pattern Anal. Mach. Intell. 20(8), 777–789 (1998)

    Article  Google Scholar 

  13. Apple pay. https://www.apple.com/apple–pay/

  14. Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings CVPR 1991, pp. 586–591, 3–6 June 1991

    Google Scholar 

  15. Juels, A.: RFID security and privacy: a research survey. IEEE J. Sel. Areas Commun. 24(2), 381–394 (2006)

    Article  MathSciNet  Google Scholar 

  16. Piltaver, R., Gjoreski, H., Gams, M.: Person identification using door accelerations. In: AITAmI 2015 Workshop, IJCAI (2015)

    Google Scholar 

  17. Kohavi, R., George, H.: Wrappers for feature subset selection. Artif. Intell. 97(1–2), 273–324 (1997)

    Article  MATH  Google Scholar 

  18. Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)

    Google Scholar 

  19. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  20. Aha, D., Kibler, D.: Instance-based learning algorithms. Mach. Learn. 6, 37–66 (1991)

    Google Scholar 

  21. Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Schoelkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods — Support Vector Learning. MIT Press, Cambridge (1998)

    Google Scholar 

  22. Bellman, R., Kalaba, R.: On adaptive control processes. IRE Trans. Autom. Control 4(2), 1–9 (1959)

    Article  Google Scholar 

  23. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)

    Article  Google Scholar 

  24. Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26(1), 43–49 (1978)

    Article  MATH  Google Scholar 

  25. Tappert, C.C., Suen, C.Y., Wakahara, T.: The state of the art in online handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 12(8), 787–808 (1990)

    Article  Google Scholar 

  26. Kuzmanic, A., Zanchi, V.: Hand shape classification using dtw and lcss as similarity measures for vision-based gesture recognition system. In: The International Conference on “Computer as a Tool” EUROCON, pp. 264–269 (2007)

    Google Scholar 

  27. Liao, T.W.: Clustering of time series data—a survey. Pattern Recogn. 38(11), 1857–1874 (2005)

    Article  MATH  Google Scholar 

  28. Salvador, S., Chan, P.: FastDTW: toward accurate dynamic time warping in linear time and space. In: KDD Workshop on Mining Temporal and Sequential Data, pp. 70–80 (2004)

    Google Scholar 

  29. Al-Naymat, G., Chawla, S., Taheri, J.: SparseDTW: a novel approach to speed up dynamic time warping (2012)

    Google Scholar 

  30. Cooley, J.W., Tukey, J.W.: An algorithm for the machine calculation of complex Fourier series. Math. Comput. 19, 297–301 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  31. Jakulin, A.: Machine learning based on attribute interactions. Ph.D. dissertation (2005)

    Google Scholar 

  32. Gams, M., Piltaver, R., Gjoreski, H.: Postopek identifikacije osebe, ki vstopa v prostor, patent P-201300281, Slovenian Intellectual Property Office, filed, 19 September 2013

    Google Scholar 

  33. Wolpert, D.: Stacked generalization. Neural Networks 5(2), 241–259 (1992)

    Article  MathSciNet  Google Scholar 

  34. Bailer-Jones, A.L.C., Smith, K.: Combining probabilities. Gaia (GAIA-C8-TN-MPIA-CBJ-053). In: Data Processing and Analysis Consortium (DPAC), issue 2 (2011)

    Google Scholar 

  35. Piltaver, R., Vodopivec, T., Gams, M.: Identifikacija oseb ob vstopu skozi vrata z uporabo pospeškomera in strojnega učenja. In: Proceedings of 16th International Multiconference Information Society, vol. A, pp. 90–93 (2013)

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank Tadej Vodopivec for recording the dataset and coding the initial version of software for data pre-processing and feature extraction. The authors would also like to thank mag. Borut Grošičar, for the discussions about the physics analysis of the door acceleration signal.

Author information

Authors and Affiliations

  1. Jožef Stefan Institute, Ljubljana, Slovenia

    Hristijan Gjoreski, Rok Piltaver & Matjaž Gams

  2. Jožef Stefan International Postgraduate School, Ljubljana, Slovenia

    Rok Piltaver & Matjaž Gams

Authors
  1. Hristijan Gjoreski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rok Piltaver
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matjaž Gams
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hristijan Gjoreski .

Editor information

Editors and Affiliations

  1. Philips Research, Eindhoven, The Netherlands

    Boris De Ruyter

  2. Hellenic Open University, Patras, Greece

    Achilles Kameas

  3. Institute of Thessaloniki, Alexander Technological Educational, Sindos, Greece

    Periklis Chatzimisios

  4. Hellenic Open University, Patras, Greece

    Irene Mavrommati

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Gjoreski, H., Piltaver, R., Gams, M. (2015). Person Identification by Analyzing Door Accelerations in Time and Frequency Domain. In: De Ruyter, B., Kameas, A., Chatzimisios, P., Mavrommati, I. (eds) Ambient Intelligence. AmI 2015. Lecture Notes in Computer Science(), vol 9425. Springer, Cham. https://doi.org/10.1007/978-3-319-26005-1_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-26005-1_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26004-4

  • Online ISBN: 978-3-319-26005-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics