Abstract
This paper describes methods and sensor technology used to identify persons from their walking characteristics. We use an array of simple binary switch floor sensors to detect footsteps. Feature analysis and recognition are performed with a fully discriminative Bayesian approach using a Gaussian Process (GP) classifier. We show the usefulness of our probabilistic approach on a large data set consisting of walking sequences of nine different subjects. In addition, we extract novel features and analyse practical issues such as the use of different shoes and walking speeds, which are usually missed in this kind of experiment. Using simple binary sensors and the large nine-person data set, we were able to achieve promising identification results: a 64% total recognition rate for single footstep profiles and an 84% total success rate using longer walking sequences (including 5 - 7-footstep profiles). Finally, we present a context-aware prototype application. It uses person identification and footstep location information to provide reminders to a user.
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References
Essa, I.A.: Ubiquitous sensing for smart and aware environments: Technologies towards the building of an aware home. IEEE Personal Communications, Special issue on networking the physical world, 47–49 (October 2000)
Brummit, B., Meyers, B., Krumm, J., Kern, A., Shafer, S.: Easyliving: Technologies for intelligent environments. In: 2nd International Symposium of Handheld and Ubiquitous Computing (HUC), New York, USA, pp. 12–29. Springer, Heidelberg (2000)
Pentland, A.: Smart rooms. Scientific American 274, 68–76 (1996)
Rasmussen, C.E., Williams, C.K.I.: Machine Learning for Gaussian Processes. MIT Press, USA (2006)
Orr, R.J., Abowd, G.D.: The smart floor: A mechanism for natural user identification and tracking. In: Proceedings of 2000 Conf. Human Factors in Computing Systems (CHI), The Hague, Netherlands, pp. 275–276. ACM Press, New York (2000)
Addlesee, M.D., Jones, A., Livesey, F., Samaria, F.: ORL active floor. IEEE Personal Communications 4(5), 35–41 (1997)
Cattin, P.: Biometric Authentication System Using Human Gait. PhD thesis, ETH-Zürich, Institute of Robotics, Switzerland (2002)
Rodriguez, R.V., Lewis, R.P., Mason, J.S.D., Evans, N.W.D.: Footstep recognition for a samrt home environment. International Journal of Smart Home 2(2), 95–110 (2008)
Suutala, J., Röning, J.: Methods for person identification on a pressure-sensitive floor: Experiments with multiple classifiers and reject option. Information Fusion Journal, Special Issue on Applications of Ensemble Methods 9, 21–40 (2008)
Yun, J.-S., Lee, S.-H., Woo, W.-T., Ryu, J.-H.: The user identification system using walking pattern over the ubifloor. In: Proceedings of International Conference on Control, Automation, and Systems (ICCAS), Gyeongju, Korea (October 2003)
Middleton, L., Buss, A.A., Bazin, A., Nixon, M.S.: A floor sensor system for gait recognition. In: Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID 2005), pp. 171–176 (2005)
Murakita, T., Ikeda, T., Ishiguro, H.: Human tracking using floor sensors based on the markov chain monte carlo method. In: Proceedings of Seventeenth International Conference on Pattern Recognition (ICPR), Cambridge, UK, August 2004, pp. 917–920 (2004)
Morishita, H., Fukui, R., Sato, T.: High resolution pressure sensor distributed floor for future human-robot symbiosis environment. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, EPFL, Lausanne, Switzerland, pp. 1246–1251 (October 2002)
Sudo, K., Yamato, J., Tomono, A.: Determining gender of walking people using multiple sensors. In: Proceedings of the IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems, December 8-11, 1996, pp. 641–646 (1996)
Eng, K., Douglas, R.J., Verschure, P.F.M.J.: An interactive space that learns to influence of human behaviour. IEEE Transaction on Systems, Man, and Cybernetics-Part A: Systems and Humans 35(1), 66–77 (2005)
Headon, R., Curwen, R.: Recognizing movements from the ground reaction force. In: Proceedings of the Workshop on Perceptive User Interfaces, Orlando, Florida, USA, November 15-16, 2001, pp. 1–8 (2001)
Köhle, M., Merk, D.: Clinical gait analysis by neural networks: Issues and experiences. In: Proceedings of the IEEE Symposium on Computer-Based Medical Systems, Maribor, Slovenia, pp. 138–143 (1997)
Vstone corporation, http://www.vstone.co.jp/e/etop.html
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, UK (2000)
Schölkopf, B., Smola, A.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, USA (2001)
Platt, J.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Smola, A., Bartlett, P., Schölkopf, B., Schuurmans, D. (eds.) Advances in Kernel Methods - Support Vector Learning, pp. 61–74. MIT Press, Cambridge (1999)
Girolami, M., Rogers, S.: Variational bayesian multinomial probit regression with gaussian process priors. Neural Computation 18, 1790–1817 (2006)
MacKay, D.J.C.: Information Theory, Inference, and Learning Algorithms. Cambridge University Press, UK (2003)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice-Hall, Englewood Cliffs (2002)
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)
Lama, N., Girolami, M.: Vbmp: Variational bayesian multinomial probit regression for multi-class classification in R. Bioinformatics (2008)
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Suutala, J., Fujinami, K., Röning, J. (2008). Gaussian Process Person Identifier Based on Simple Floor Sensors. In: Roggen, D., Lombriser, C., Tröster, G., Kortuem, G., Havinga, P. (eds) Smart Sensing and Context. EuroSSC 2008. Lecture Notes in Computer Science, vol 5279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88793-5_5
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DOI: https://doi.org/10.1007/978-3-540-88793-5_5
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