TY - GEN
T1 - EarHover
T2 - 37th Annual ACM Symposium on User Interface Software and Technology, UIST 2024
AU - Suzuki, Shunta
AU - Amesaka, Takashi
AU - Watanabe, Hiroki
AU - Shizuki, Buntarou
AU - Sugiura, Yuta
N1 - Publisher Copyright:
© 2024 Owner/Author.
PY - 2024/10/13
Y1 - 2024/10/13
N2 - 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.
AB - 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.
KW - Doppler effect
KW - Hearables
KW - acoustic sensing
KW - deep learning
KW - mid-air gesture recognition
KW - sound leakage
UR - https://www.scopus.com/pages/publications/85215065403
UR - https://www.scopus.com/pages/publications/85215065403#tab=citedBy
U2 - 10.1145/3654777.3676367
DO - 10.1145/3654777.3676367
M3 - Conference contribution
AN - SCOPUS:85215065403
T3 - UIST 2024 - Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology
BT - UIST 2024 - Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology
PB - Association for Computing Machinery, Inc
Y2 - 13 October 2024 through 16 October 2024
ER -