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Sensors 2011, 11(3), 2611-2639;

User Identification Using Gait Patterns on UbiFloorII

U-embedded Convergence Research Center, Korea Electronics Technology Institute, 68 Yatap-dong Bundang-gu, Seongnam, Korea
Received: 10 January 2011 / Revised: 7 February 2011 / Accepted: 22 February 2011 / Published: 1 March 2011
(This article belongs to the Special Issue Advanced Embedded Sensors)
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This paper presents a system of identifying individuals by their gait patterns. We take into account various distinguishable features that can be extracted from a user’s gait and then divide them into two classes: walking pattern and stepping pattern. The conditions we assume are that our target environments are domestic areas, the number of users is smaller than 10, and all users ambulate with bare feet considering the everyday lifestyle of the Korean home. Under these conditions, we have developed a system that identifies individuals’ gait patterns using our biometric sensor, UbiFloorII. We have created UbiFloorII to collect walking samples and created software modules to extract the user’s gait pattern. To identify the users based on the gait patterns extracted from walking samples over UbiFloorII, we have deployed multilayer perceptron network, a feedforward artificial neural network model. The results show that both walking pattern and stepping pattern extracted from users’ gait over the UbiFloorII are distinguishable enough to identify the users and that fusing two classifiers at the matching score level improves the recognition accuracy. Therefore, our proposed system may provide unobtrusive and automatic user identification methods in ubiquitous computing environments, particularly in domestic areas. View Full-Text
Keywords: user identification; gait recognition; walking pattern; stepping pattern; UbiFloorII user identification; gait recognition; walking pattern; stepping pattern; UbiFloorII

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Yun, J. User Identification Using Gait Patterns on UbiFloorII. Sensors 2011, 11, 2611-2639.

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