Abstract
This paper reports experiments of recognizing walkers based on measurements with a pressure-sensitive EMFi-floor. Identification is based on a two-level classifier system. The first level performs Learning Vector Quantization (LVQ) with a reject option to identify or to reject a single footstep. The second level classifies or rejects a sequence of three consecutive identified footsteps based on the knowledge from the first level. The system was able to reduce classification error compared to a single footstep classifier without a reject option. The results show a 90% overall success rate with a 20% rejection rate, identifying eleven walkers, which can be considered very reliable.
This work was funded by TEKES and Academy of Finland
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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 (October 2000), Special issue on networking the physical world
Addlesee, M.D., Jones, A., Livesey, F., Samaria, F.: ORL active floor. IEEE Personal Communications 4(5), 35–41 (1997)
Orr, R.J., Abowd, G.D.: The smart floor: A mechanism for natural user identification and tracking. In: Proc. 2000 Conf. Human Factors in Computing Systems (CHI 2000), ACM Press, New York (2000)
Pirttikangas, S., Suutala, J., Riekki, J., Röning, J.: Footstep identification from pressure signals using Hidden Markov Models. In: Proc. Finnish Signal Processing Symposium, FINSIG 2003, May 2003, pp. 124–128 (2003)
Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1997)
Pirttikangas, S., Suutala, J., Riekki, J., Röning, J.: Learning vector quantization in footstep identification. In: Hamza, M.H. (ed.) Proc. 3rd IASTED International Conference on Artificial Intelligence and Applications (AIA 2003), Benalmadena, Spain, IASTED, September 8-10, pp. 413–417, ACTA Press (2003)
Paajanen, M., Lekkala, J., Kirjavainen, K.: Electromechanical film (EMFi) - a new multipurpose electret material. Sensors and actuators A 84(1-2) (August 2000)
De Stefano, C., Sansone, C., Vento, M.: To reject or not to reject: That is the question - an answer in case of neural classifiers. IEEE Transactions on Systems, Man, and Cybernetics part c: Applications and Reviews 30(1), 84–94 (2000)
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)
Ho, T.K.: Recognition of handwritten digits by combining independent learning vector quantization. In: Proceedings of the Second International Conference on Document Analysis and Recognition, pp. 818–821 (October 1993)
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Suutala, J., Pirttikangas, S., Riekki, J., Röning, J. (2004). Reject-Optional LVQ-Based Two-Level Classifier to Improve Reliability in Footstep Identification. In: Ferscha, A., Mattern, F. (eds) Pervasive Computing. Pervasive 2004. Lecture Notes in Computer Science, vol 3001. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24646-6_12
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DOI: https://doi.org/10.1007/978-3-540-24646-6_12
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