Abstract
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.
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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. https://doi.org/10.1109/CCNC.2011.5766464
Avila, L., Bailey, M.: The wearable revolution. IEEE Comput. Graph. Appl. 35(2), 104–104 (2015). https://doi.org/10.1109/MCG.2015.44
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)
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). https://doi.org/10.1145/2994374.2994393
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). https://doi.org/10.1093/comjnl/bxw111
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. https://doi.org/10.1109/IIHMSP.2010.83
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). https://doi.org/10.3390/app7100986, http://www.mdpi.com/2076-3417/7/10/986
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. https://doi.org/10.1109/AUTOID.2007.380623
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). https://doi.org/10.1109/TBME.2013.2250972
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. https://doi.org/10.1109/BTAS.2015.7358794
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)
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)
Muaaz, M., Mayrhofer, R.: Smartphone-based gait recognition: from authentication to imitation. IEEE Trans. Mob. Comput. 16(11), 3209–3221 (2017). https://doi.org/10.1109/TMC.2017.2686855
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)
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). https://doi.org/10.1109/MAES.2013.6642829
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. https://doi.org/10.1109/IIH-MSP.2012.11
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). https://doi.org/10.1007/978-981-10-7245-1_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). https://doi.org/10.1109/RBME.2017.2747402
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)
Seneviratne, S., et al.: A survey of wearable devices and challenges. IEEE Commun. Surv. Tutorials 19(4), 2573–2620 (2017). https://doi.org/10.1109/COMST.2017.2731979
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)
Tax, D.: DDtools, the Data Description Toolbox for Matlab, January 2018, version 2.1.3
Vecchio, A., Cola, G.: Fall detection using ultra-wideband positioning. In: 2016 IEEE Sensors, pp. 1–3, October 2016. https://doi.org/10.1109/ICSENS.2016.7808527
Vecchio, A., Mulas, F., Cola, G.: Posture recognition using the interdistances between wearable devices. IEEE Sens. Lett. 1(4), 1–4 (2017). https://doi.org/10.1109/LSENS.2017.2726759
Vecchio, A., Cola, G.: A method based on UWB for user identification during gait periods. Healthcare Technol. Lett. (2019). https://digital-library.theiet.org/content/journals/10.1049/htl.2018.5050
Acknowledgment
This work was partially funded by the Italian Ministry of Education and Research (MIUR) in the framework of the CrossLab project (Departments of Excellence).
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Brombin, L. et al. (2019). User’s Authentication Using Information Collected by Smart-Shoes. In: Mucchi, L., Hämäläinen, M., Jayousi, S., Morosi, S. (eds) Body Area Networks: Smart IoT and Big Data for Intelligent Health Management. BODYNETS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 297. Springer, Cham. https://doi.org/10.1007/978-3-030-34833-5_21
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