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. 2011;11(3):2611-39.
doi: 10.3390/s110302611. Epub 2011 Mar 1.

User identification using gait patterns on UbiFloorII

Jaeseok Yun  1
Affiliations

User identification using gait patterns on UbiFloorII

Jaeseok Yun. Sensors (Basel). 2011.

Abstract

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.

Keywords: UbiFloorII; gait recognition; stepping pattern; user identification; walking pattern.

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Figures

Figure 1.
Figure 1.
Examples of walking pattern in gait: (a) stride length, dynamic range, foot angle (b) stance time and swing time.
Figure 2.
Figure 2.
An example of stepping pattern in gait: ground reaction force.
Figure 3.
Figure 3.
Overall structure of UbiFloorII.
Figure 4.
Figure 4.
Reflective photo interrupters (left) and electric circuit (right).
Figure 5.
Figure 5.
A wooden tile composed of 64 photo interrupters (left) and UbiFloorII (right).
Figure 6.
Figure 6.
An example of searching footprints.
Figure 7.
Figure 7.
An example of a footprint model.
Figure 8.
Figure 8.
Walking feature extraction.
Figure 9.
Figure 9.
An array of transitional footprints.
Figure 10.
Figure 10.
An array of sampled transitional footprints.
Figure 11.
Figure 11.
Structure of the neural network for user identification.
Figure 12.
Figure 12.
Results of deciding the number of hidden nodes (left) and the epoch and goal (right) for walking pattern-based identification.
Figure 13.
Figure 13.
Results of deciding the number of hidden nodes (left) and the epoch and goal (right) for stepping pattern-based identification.
Figure 14.
Figure 14.
A flow chart of fusion at the matching score level for gait recognition.
See this image and copyright information in PMC

References

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