EarBuddy: Enabling On-Face Interaction via Wireless Earbuds

被引:61
作者
Xu, Xuhai [1 ,2 ]
Shi, Haitian [2 ,3 ]
Yi, Xin [2 ,4 ]
Liu, WenJia [5 ]
Yan, Yukang [2 ]
Shi, Yuanchun [2 ,4 ]
Mariakakis, Alex [3 ]
Mankoff, Jennifer [3 ]
Dey, Anind K. [1 ]
机构
[1] Univ Washington, Informat Sch & Dub Grp, Seattle, WA 98195 USA
[2] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China
[3] Univ Washington, Paul G Allen Sch Comp Sci & Engn, DUB Grp, Seattle, WA USA
[4] Minist Educ, Key Lab Pervas Comp, Beijing, Peoples R China
[5] Beijing Univ Posts & Telecommun, Dept Comp Sci & Technol, Beijing, Peoples R China
来源
PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20) | 2020年
基金
中国博士后科学基金;
关键词
Wireless earbuds; face and ear interaction; gesture recognition; NEURAL-NETWORKS;
D O I
10.1145/3313831.3376836
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页数:14
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