User’s Authentication Using Information Collected by Smart-Shoes

  • Luca Brombin
  • Margherita Gambini
  • Pietro Gronchi
  • Roberto Magherini
  • Lorenzo Nannini
  • Amedeo Pochiero
  • Alessandro Sieni
  • Alessio VecchioEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 297)


In the last years, smart-shoes moved from the medical domain, where they are used to collect gait-related data during rehabilitation or in case of pathologies, to the every-day life of an increasing number of people. In this paper, a method useful to effortlessly authenticate the user during gait periods is proposed. The method relies on the information collected by shoe-mounted accelerometers and gyroscopes, and on the distance between feet collected by Ultra-WideBand (UWB) transceivers. Experimental results show that a balanced accuracy equal to \(97\%\) can be achieved even when information about the possible impostors is not known in advance. The contribution of the different information sources, accelerometer, gyroscope, and UWB, is also evaluated.


Gait Authentication Biometrics Wearable device Smart-shoe 



This work was partially funded by the Italian Ministry of Education and Research (MIUR) in the framework of the CrossLab project (Departments of Excellence).


  1. 1.
    Abbate, S., Avvenuti, M., Cola, G., Corsini, P., Light, J., Vecchio, A.: Recognition of false alarms in fall detection systems. In: Proceedings of the IEEE Consumer Communications and Networking Conference (CCNC), pp. 23–28, January 2011.
  2. 2.
    Avila, L., Bailey, M.: The wearable revolution. IEEE Comput. Graph. Appl. 35(2), 104–104 (2015). Scholar
  3. 3.
    Brodersen, K.H., Ong, C.S., Stephan, K.E., Buhmann, J.M.: The balanced accuracy and its posterior distribution. In: Proceedings of the 20th International Conference on Pattern Recognition, pp. 3121–3124. IEEE (2010)Google Scholar
  4. 4.
    Cola, G., Avvenuti, M., Musso, F., Vecchio, A.: Gait-based authentication using a wrist-worn device. In: Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MOBIQUITOUS 2016, pp. 208–217. ACM, New York (2016).
  5. 5.
    Cola, G., Avvenuti, M., Vecchio, A.: Real-time identification using gait pattern analysis on a standalone wearable accelerometer. Comput. J. 60(8), 1173–1186 (2017). Scholar
  6. 6.
    Derawi, M.O., Nickel, C., Bours, P., Busch, C.: Unobtrusive user-authentication on mobile phones using biometric gait recognition. In: Proceedings of the Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 306–311, October 2010.
  7. 7.
    Eskofier, B.M., et al.: An overview of smart shoes in the Internet of health things: gait and mobility assessment in health promotion and disease monitoring. Appl. Sci. 7(10) (2017)., Scholar
  8. 8.
    Gafurov, D., Snekkenes, E., Bours, P.: Gait authentication and identification using wearable accelerometer sensor. In: Proceedings of the IEEE Workshop on Automatic Identification Advanced Technologies, pp. 220–225, June 2007.
  9. 9.
    Howell, A.M., Kobayashi, T., Hayes, H.A., Foreman, K.B., Bamberg, S.J.M.: Kinetic gait analysis using a low-cost insole. IEEE Trans. Biomed. Eng. 60(12), 3284–3290 (2013). Scholar
  10. 10.
    Johnston, A.H., Weiss, G.M.: Smartwatch-based biometric gait recognition. In: Proceedings of the IEEE International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1–6, September 2015.
  11. 11.
    Kim, J., Lee, K.B., Hong, S.G.: Random forest based-biometric identification using smart shoes. In: Proceedings of the Eleventh International Conference on Sensing Technology (ICST), pp. 1–4. IEEE (2017)Google Scholar
  12. 12.
    Kumar, R., Kundu, P.P., Phoha, V.V.: Continuous authentication using one-class classifiers and their fusion. In: Proceedings of the IEEE International Conference on Identity, Security, and Behavior Analysis (ISBA), pp. 1–8. IEEE (2018)Google Scholar
  13. 13.
    Muaaz, M., Mayrhofer, R.: Smartphone-based gait recognition: from authentication to imitation. IEEE Trans. Mob. Comput. 16(11), 3209–3221 (2017). Scholar
  14. 14.
    Ngo, T.T., Makihara, Y., Nagahara, H., Mukaigawa, Y., Yagi, Y.: The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication. Pattern Recogn. 47(1), 228–237 (2014)CrossRefGoogle Scholar
  15. 15.
    Nickel, C., Busch, C.: Classifying accelerometer data via Hidden Markov Models to authenticate people by the way they walk. IEEE Aerosp. Electron. Syst. Mag. 28(10), 29–35 (2013). Scholar
  16. 16.
    Nickel, C., Wirtl, T., Busch, C.: Authentication of smartphone users based on the way they walk using k-NN algorithm. In: Proceedings of the Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 16–20, July 2012.
  17. 17.
    Oak, R.: A literature survey on authentication using behavioural biometric techniques. In: Bhalla, S., Bhateja, V., Chandavale, A.A., Hiwale, A.S., Satapathy, S.C. (eds.) Intelligent Computing and Information and Communication. AISC, vol. 673, pp. 173–181. Springer, Singapore (2018). Scholar
  18. 18.
    Ramirez-Bautista, J.A., Huerta-Ruelas, J.A., Chaparro-Cárdenas, S.L., Hernández-Zavala, A.: A review in detection and monitoring gait disorders using in-shoe plantar measurement systems. IEEE Rev. Biomed. Eng. 10, 299–309 (2017). Scholar
  19. 19.
    Schürmann, D., Brüsch, A., Sigg, S., Wolf, L.: BANDANA - body area network device-to-device authentication using natural gait. In: Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 190–196. IEEE (2017)Google Scholar
  20. 20.
    Seneviratne, S., et al.: A survey of wearable devices and challenges. IEEE Commun. Surv. Tutorials 19(4), 2573–2620 (2017). Scholar
  21. 21.
    Sun, F., Mao, C., Fan, X., Li, Y.: Accelerometer-based speed-adaptive gait authentication method for wearable iot devices. IEEE Internet Things J. 6(1), 820–830 (2019)CrossRefGoogle Scholar
  22. 22.
    Tax, D.: DDtools, the Data Description Toolbox for Matlab, January 2018, version 2.1.3Google Scholar
  23. 23.
    Vecchio, A., Cola, G.: Fall detection using ultra-wideband positioning. In: 2016 IEEE Sensors, pp. 1–3, October 2016.
  24. 24.
    Vecchio, A., Mulas, F., Cola, G.: Posture recognition using the interdistances between wearable devices. IEEE Sens. Lett. 1(4), 1–4 (2017). Scholar
  25. 25.
    Vecchio, A., Cola, G.: A method based on UWB for user identification during gait periods. Healthcare Technol. Lett. (2019).

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.University of PisaPisaItaly

Personalised recommendations