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Shoe Recognition Model with Floor Pressure Sensors

Authors:
Sora Kamimura
Tetsuo Yutani
Atsuko Shibuya
Tsubasa Yumura

Keywords: Neural Networks, Flow Line Analysis, Pressure-Sensitive Conductive Sheet.

Abstract:
In research on traffic flow analysis, computer vision methods using camera images have been the predominant approach. However, using cameras for flow line analysis presents challenges, such as creating blind spots caused by obstructions such as people or objects. Additionally, privacy concerns arise. These issues can be mitigated using floor pressure sensors for flow line analysis. To successfully perform flow line analysis with these sensors, it is essential to identify individuals based on factors such as weight, stride length, speed, and shoe type. In this study, we developed a system to identify shoe types from footprint pressure distribution using a neural network model. Our focus was on three types of shoes: sneakers, room shoes, and sandals. We collected data for each category and created a recognition model, achieving an F-measure of 97.6% in the best model. The primary challenges for practical implementation are measurement time and durability.

Pages: 10 to 11

Copyright: Copyright (c) IARIA, 2024

Publication date: November 3, 2024

Published in: conference

ISSN: 2308-4405

ISBN: 978-1-68558-207-4

Location: Nice, France

Dates: from November 3, 2024 to November 7, 2024