EarBuddy: Enabling On-Face Interaction via Wireless Earbuds

要旨

Past research regarding on-body interaction typically requires custom sensors, limiting their scalability and generalizability. We propose EarBuddy, a real-time system that leverages the microphone in commercial wireless earbuds to detect tapping and sliding gestures near the face and ears. We develop a design space to generate 27 valid gestures and conducted a user study (N=16) to select the eight gestures that were optimal for both human preference and microphone detectability. We collected a dataset on those eight gestures (N=20) and trained deep learning models for gesture detection and classification. Our optimized classifier achieved an accuracy of 95.3%. Finally, we conducted a user study (N=12) to evaluate EarBuddy's usability. Our results show that EarBuddy can facilitate novel interaction and that users feel very positively about the system. EarBuddy provides a new eyes-free, socially acceptable input method that is compatible with commercial wireless earbuds and has the potential for scalability and generalizability

キーワード
Wireless earbuds
face and ear interaction
gesture recognition
著者
Xuhai Xu
University of Washington & Tsinghua University, Seattle, WA, USA
Haitian Shi
Tsinghua University & University of Washington, Seattle, WA, USA
Xin Yi
Tsinghua University & Key Laboratory of Pervasive Computing, Ministry of Education, Beijing, China
WenJia Liu
Beijing University of Posts and Telecommunications, Beijing, China
Yukang Yan
Tsinghua University, Beijing, China
Yuanchun Shi
Tsinghua University & Key Laboratory of Pervasive Computing, Ministry of Education, Beijing, China
Alex Mariakakis
University of Washington, Seattle, WA, USA
Jennifer Mankoff
University of Washington, Seattle, WA, USA
Anind K. Dey
University of Washington, Seattle, WA, USA
DOI

10.1145/3313831.3376836

動画

会議: CHI 2020

The ACM CHI Conference on Human Factors in Computing Systems

セッション: On-body interaction

Paper session
Paper
311 KAUA'I
2020-04-27 23:00:00
2020-04-28 00:15:00
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