NFCGest: Gesture Interaction with NFC terminal Enabled by Super High Speed ADC

Hand Gesture RecognitionVoice User Interface (VUI) DesignPasswords & AuthenticationSoftware Engineers & DevelopersAI/ML Researchers & Engineers

Near Field Communication (NFC) is a widely applied technology embedded in credit cards, smartphones, and identity credentials like passports. By tapping a media to an NFC terminal, users can authorize payment transactions, gain access to spaces, and authenticate their identity. However, beyond tapping, current NFC application protocols define no other interactions, leaving significant room for exploration. In this work, we show that additional interactions can be enabled by analyzing the raw RX analog signals, which operate at high frequencies (848 kHz), using the test functions of an NFC terminal. We then sampled such signals using a custom low-cost, high-speed streaming ADC, enabling real-time streaming and visualization of the signals on an amplitude-phase plot. As users move the NFC media over the antenna, the raw signals form characteristic 2D curves on this plot. Accordingly, we identified three categories of card interactions: swipe, tap, and shake. By introducing asymmetric interference coils, we can further enable directional interactions. We showcase a set of 9 gestures based on these interaction categories and evaluated them in a ten-participant user study. Our classification model demonstrates cross-user accuracy of 91.8%, validating both our real-time processing pipeline and gesture design. To demonstrate the practical value of NFCGest, we propose applications for both display-based and display-less NFC terminals. We highlight purely contactless gesture interaction for display-based systems, and emphasize the enriched interaction space for display-less sensors.

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https://hci.top/en/papers/uist/206834/2025

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DOI: https://doi.org/10.1145/3746059.3747729
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Source
UIST
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Year
2025
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Authors
2 authors
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Subtopics
Hand Gesture Recognition, Voice User Interface (VUI) Design, Passwords & Authentication
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Software Engineers & Developers, AI/ML Researchers & Engineers
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Abstract only
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