タッチ不要のNFC PIN入力技術を開発、衛生性・アクセシビリティを向上(Forget numbers: your PIN could consist of a shimmy and a shake)

2025-09-29 カナダ・ブリティッシュコロンビア大学(UBC)

Web要約 の発言:
ブリティッシュコロンビア大学(UBC)の研究者は、**NFC(近距離無線通信)**を活用し、PIN入力やチップ選択を「スワイプ」「タップ」などの身振りで行える新技術を開発した。カードやスマホを決済端末の銅コイル付きリーダーにかざすと磁場が変化し、AIモデルがその動きを9種類のジェスチャーとして92%の精度で判別できる。これにより、非接触で衛生的かつ高速な支払いが可能となり、ボタン操作が困難な人の利用も支援できる。導入コストは端末1台あたり20ドル以下とされ、セキュリティ強化の効果も期待される。現在、特許出願も進行中。

タッチ不要のNFC PIN入力技術を開発、衛生性・アクセシビリティを向上(Forget numbers: your PIN could consist of a shimmy and a shake)
This NFC-based prototype could enable gesture pincodes in the future. Credit: Alex Walls

<関連情報>

NFCGest: NFCデバイスによる非接触ジェスチャーインタラクション NFCGest: Contactless Gestural Interactions with NFC Devices

Bu Li, Robert Xiao
UIST ’25: Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology  Published: 27 September 2025
DOI:https://doi.org/10.1145/3746059.3747729

Abstract

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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