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| Content Provider | ACM Digital Library |
|---|---|
| Author | Ramsay, Andrew Rogers, Simon Mohd Noor, Mohammad Faizuddin Hughes, Stephen Williamson, John Murray-Smith, Roderick |
| Abstract | We demonstrate that front-of-screen targeting on mobile phones can be predicted from back-of-device grip manipulations. Using simple, low-resolution capacitive touch sensors placed around a standard phone, we outline a machine learning approach to modelling the grip modulation and inferring front-of-screen touch targets. We experimentally demonstrate that grip is a remarkably good predictor of touch, and we can predict touch position 200ms before contact with an accuracy of 18mm. |
| Starting Page | 2005 |
| Ending Page | 2008 |
| Page Count | 4 |
| File Format | PDF MP4 |
| ISBN | 9781450324731 |
| DOI | 10.1145/2556288.2557148 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-04-26 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Machine learning Back-of-device Touch Capacitive |
| Content Type | Audio Text |
| Resource Type | Article |
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