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EarHover: Mid-Air Gesture Recognition for Hearables Using Sound Leakage Signals

  • Shunta Suzuki
  • , Takashi Amesaka
  • , Hiroki Watanabe
  • , Buntarou Shizuki
  • , Yuta Sugiura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We introduce EarHover, an innovative system that enables mid-air gesture input for hearables. Mid-air gesture input, which eliminates the need to touch the device and thus helps to keep hands and the device clean. However, existing mid-air gesture input methods for hearables have been limited to adding cameras or infrared sensors. By focusing on the sound leakage phenomenon unique to hearables, we have realized mid-air gesture recognition using a speaker and an external microphone that are highly compatible with hearables. The signal leaked to the outside of the device due to sound leakage can be measured by an external microphone, which detects the differences in reflection characteristics caused by the hand's speed and shape during mid-air gestures. Among 27 types of gestures, we determined the seven suitable gestures for EarHover in terms of signal discrimination and user acceptability. We then evaluated the gesture detection and classification performance of two prototype devices (in-ear type/open-ear type) for real-world application scenarios.

Original languageEnglish
Title of host publicationUIST 2024 - Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9798400706288
DOIs
Publication statusPublished - 2024 Oct 13
Event37th Annual ACM Symposium on User Interface Software and Technology, UIST 2024 - Pittsburgh, United States
Duration: 2024 Oct 132024 Oct 16

Publication series

NameUIST 2024 - Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology

Conference

Conference37th Annual ACM Symposium on User Interface Software and Technology, UIST 2024
Country/TerritoryUnited States
CityPittsburgh
Period24/10/1324/10/16

Keywords

  • Doppler effect
  • Hearables
  • acoustic sensing
  • deep learning
  • mid-air gesture recognition
  • sound leakage

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Software

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