Learning Vector Quantization in Footstep Identification

S. Pirttikangas, J. Suutala, J. Riekki, and J. Röning (Finland)


Intelligent Environments, Personal Profiling


This paper reports experiments on recognizing walk ers from measurements with a pressure-sensitive floor, more specifically, a floor covered with EMFi material. A 100 square meter pressure-sensitive floor (EMFi floor) was recently installed in the Intelligent Systems Group's re search laboratory at the University of Oulu as part of a smart living room. The floor senses the changes in the pres sure against its surface and produces voltage signals of the event. The test set for footstep identification includes EMFi data from 11 walkers. The steps were extracted from the data and featurized. Identification was made with Learning Vector Quantization. Discarding a known error type in the measurements, the results show a 78 % overall success rate of footstep identification and are hence very promising.

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