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In this paper we present SurfSole, an untethered mobile system combining a smart sole prototype and mobile app to achieve real-time surface identification. We solely rely on the technology of capacitive sensing while choosing a neural network approach for classification. We evaluated different machine learning models with different layer architectures of 3–4 layers with 32, 64, 128, and 256 filters. The theoretical overall accuracy reaches from 74.85% up to 87.11%. While we retrieve data with 40 Hz with a single window of 120 data points, we have a real-time detection delay of 3 s.

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SurfSole: Demonstrating Real-Time Surface Identification via Capacitive Sensing with Neural Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Patrick Willnow,
  • Max Sternitzke,
  • Ruben Schlonsak,
  • Marco Gabrecht,
  • Denys J. C. Matthies

摘要

In this paper we present SurfSole, an untethered mobile system combining a smart sole prototype and mobile app to achieve real-time surface identification. We solely rely on the technology of capacitive sensing while choosing a neural network approach for classification. We evaluated different machine learning models with different layer architectures of 3–4 layers with 32, 64, 128, and 256 filters. The theoretical overall accuracy reaches from 74.85% up to 87.11%. While we retrieve data with 40 Hz with a single window of 120 data points, we have a real-time detection delay of 3 s.