ThumbSense: automatic input mode sensing for touchpad-based interactions
CHI '03 Extended Abstracts on Human Factors in Computing Systems
Dictionary attacks using keyboard acoustic emanations
Proceedings of the 13th ACM conference on Computer and communications security
Touch&Type: a novel pointing device for notebook computers
Proceedings of the 4th Nordic conference on Human-computer interaction: changing roles
Stane: synthesized surfaces for tactile input
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Scratch input: creating large, inexpensive, unpowered and mobile finger input surfaces
Proceedings of the 21st annual ACM symposium on User interface software and technology
Keyboard acoustic emanations revisited
ACM Transactions on Information and System Security (TISSEC)
A practical pressure sensitive computer keyboard
Proceedings of the 22nd annual ACM symposium on User interface software and technology
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Touch-display keyboards: transforming keyboards into interactive surfaces
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Surfboard: keyboard with microphone as a low-cost interactive surface
UIST '10 Adjunct proceedings of the 23nd annual ACM symposium on User interface software and technology
TapSense: enhancing finger interaction on touch surfaces
Proceedings of the 24th annual ACM symposium on User interface software and technology
Augmenting touch interaction through acoustic sensing
Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces
Acoustic barcodes: passive, durable and inexpensive notched identification tags
Proceedings of the 25th annual ACM symposium on User interface software and technology
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We present a directional and quantitative input method by clawing key tops, Keyboard Clawing. The method allows a user to input a direction and quantity at the same time without moving his/her hands much from the keyboard's home position. As a result, the user can seamlessly continue typing before and after inputting the direction and quantity. We found that clawing direction is classified using clawing sounds with an accuracy of 68.2% and that our method can be used to input rough quantity.