Name
Abstract | ||
|---|---|---|
This paper finds that actions of the teeth, namely tapping and sliding, produce vibrations in the jaw and skull. These vibrations are strong enough to propagate to the edge of the face and produce vibratory signals at an earphone. By re-tasking the earphone speaker as an input transducer - a software modification in the sound card - we are able to sense teeth-related gestures across various models of ear/headphones. In fact, by analyzing the signals at the two earphones, we show the feasibility of also localizing teeth gestures, resulting in a human-to-machine interface. Challenges range from coping with weak signals, distortions due to different teeth compositions, lack of timing resolution, spectral dispersion, etc. We address these problems with a sequence of sensing techniques, resulting in the ability to detect 6 distinct gestures in real-time. Results from 18 volunteers exhibit robustness, even though our system - EarSense - does not depend on per-user training. Importantly, EarSense also remains robust in the presence of concurrent user activities, like walking, nodding, cooking and cycling. Our ongoing work is focused on detecting teeth gestures even while music is being played in the earphone; once that problem is solved, we believe EarSense could be even more compelling.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3372224.3419197 | MobiCom '20: The 26th Annual International Conference on Mobile Computing and Networking
London
United Kingdom
September, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7085-1 | 1 |
PageRank | References | Authors |
0.35 | 19 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. Prakash | 1 | 10 | 3.91 |
| Z. Yang | 2 | 10 | 1.65 |
| Y. Wei | 3 | 2 | 0.71 |
| H. Hassanieh | 4 | 591 | 37.63 |
| Romit Roy Choudhury | 5 | 3951 | 233.31 |