The generalized perceived input point model and how to double touch accuracy by extracting fingerprints

By Christian Holz, Patrick Baudisch. International Conference on Human Factors in Computing Systems, 2010

297 citations recorded by the source corpus.

It is generally assumed that touch input cannot be accurate because of the fat finger problem, i.e., the softness of the fingertip combined with the occlusion of the target by the finger. In this paper, we show that this is not the case. We base our argument on a new model of touch inaccuracy. Our model is not based on the fat finger problem, but on the perceived input point model. In its published form, this model states that touch screens report touch location at an offset from the intended target. We generalize this model so that it represents offsets for individual finger postures and users. We thereby switch from the traditional 2D model of touch to a model that considers touch a phenomenon in 3-space. We report a user study, in which the generalized model explained 67% of the touch inaccuracy that was previously attributed to the fat finger problem. In the second half of this paper, we present two devices that exploit the new model in order to improve touch accuracy. Both model touch on per-posture and per-user basis in order to increase accuracy by applying respective offsets. Our RidgePad prototype extracts posture and user ID from the user's fingerprint during each touch interaction. In a user study, it achieved 1.8 times higher accuracy than a simulated capacitive baseline condition. A prototype based on optical tracking achieved even 3.3 times higher accuracy. The increase in accuracy can be used to make touch interfaces more reliable, to pack up to 3.32 > 10 times more controls into the same surface, or to bring touch input to very small mobile devices.

Subfields: Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/0f3178aaa04a423897c06b75318e411bb17b8679

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Citations in the corpus

Using a depth camera as a touch sensor (2010, background): “It is important to note that calculating the position of the target was very simplistic, and did not incorporate adjustments to more closely match users’ expectations (see [8]).”
NormalTouch and TextureTouch: High-fidelity 3D Haptic Shape Rendering on Handheld Virtual Reality Controllers (2016, background): “…sizes for ‘reliable buttons’ (targets that capture 95% of all input) at a magnitude of 7–8 mm, which compares to the 8.2mm diameter of minimum reliable targets in related touch studies (when controlled for the finger’s yaw, pitch, and roll angle [16]), which the task in our study loosely included.”
OmniTouch: wearable multitouch interaction everywhere (2011, methodology): “For maximum generality, we did not compute or apply any per-user offset, though this has been shown to significantly increase accuracy [9,30].”
GAVIN (2021, background): “…selecting text for annotating digital documents on mobile devices could be challenging for readers due to the difficulties introduced by the “fat finger” problem [27], and because it breaks the reading flow and diverts the reader’s attention from the primary task of reading and note-taking.”