GelForce: a vision-based traction field computer interface
CHI '05 Extended Abstracts on Human Factors in Computing Systems
Low-cost multi-touch sensing through frustrated total internal reflection
Proceedings of the 18th annual ACM symposium on User interface software and technology
ForceTile: tabletop tangible interface with vision-based force distribution sensing
ACM SIGGRAPH 2008 new tech demos
PuyoSheet and PuyoDots: simple techniques for adding "button-push" feeling to touch panels
CHI '09 Extended Abstracts on Human Factors in Computing Systems
Detecting and leveraging finger orientation for interaction with direct-touch surfaces
Proceedings of the 22nd annual ACM symposium on User interface software and technology
Hand distinction for multi-touch tabletop interaction
Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces
Z-touch: an infrastructure for 3d gesture interaction in the proximity of tabletop surfaces
ACM International Conference on Interactive Tabletops and Surfaces
Force gestures: augmented touch screen gestures using normal and tangential force
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Usage and recognition of finger orientation for multi-touch tabletop interaction
INTERACT'11 Proceedings of the 13th IFIP TC 13 international conference on Human-computer interaction - Volume Part III
Evaluation of human tangential force input performance
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Using shear as a supplemental two-dimensional input channel for rich touchscreen interaction
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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"WrinkleSurface", which we developed by attaching a gel sheet to a FTIR-based touchscreen, enables a user to perform novel touch motions such as Push, Thrust, and Twist_CW (clockwise), and Twist_CCW (counterclockwise). Our research is focused on the evaluation of this soft-surfaced multi-touch interface. Specifically, to examine how a user can input our novel input methods precisely, we evaluated the user's performance of each method by two to nine levels of target acquisition task. As a result, we found some points to be improved in our recognition algorithm in order to increase the success rate of Push and Thrust. In addition, a user can input Twist before the level of six because the success rate of Twist was high up to that level.