The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Understanding touch
Abstract
Current touch devices, such as capacitive touchscreens are based on the implicit assumption that users acquire targets with the center of the contact area between finger and device. Findings from our previous work indicate, however, that such devices are subject to systematic error offsets. This suggests that the underlying assumption is most likely wrong. In this paper, we therefore revisit this assumption. In a series of three user studies, we find evidence that the features that users align with the target are visual features. These features are located on the top of the user's fingers, not at the bottom, as assumed by traditional devices. We present the projected center model, under which error offsets drop to 1.6mm, compared to 4mm for the traditional model. This suggests that the new model is indeed a good approximation of how users conceptualize touch input. The primary contribution of this paper is to help understand touch-one of the key input technologies in human-computer interaction. At the same time, our findings inform the design of future touch input technology. They explain the inaccuracy of traditional touch devices as a -Sparallax- artifact between user control based on the top of the finger and sensing based on the bottom side of the finger. We conclude that certain camera-based sensing technologies can inherently be more accurate than contact area-based sensing.
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Cites
The information capacity of the human motor system in controlling the amplitude of movement.
Fitts, P. M. (1954) · Journal of Experimental Psychology · doi:10.1037/h0055392 · Cited by 6,466 · cited 1×
“Fitts’ Law models the targeting time for one-dimensional targets [8].”
BackgroundShift: a technique for operating pen- based interfaces using touch
Vogel, D., & Baudisch, P. (2007) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1240624.1240727 · Cited by 486
Self-citation
Cited by
Effects of Language Modeling and its Personalization on Touchscreen Typing Performance
Fowler, A., Partridge, K., Chelba, C., Bi, X., Ouyang, T., & Zhai, S. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702503 · Cited by 127
JustCorrect: Intelligent Post Hoc Text Correction Techniques on Smartphones
Cui, W., Zhu, S., Zhang, M. R., Schwartz, H. A., Wobbrock, J. O., & Bi, X. (2020) · Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology · doi:10.1145/3379337.3415857 · Cited by 24
Effects of Aging on Small Target Selection with Touch Input
Sultana, A., & Moffatt, K. (2019) · ACM Transactions on Accessible Computing · doi:10.1145/3300178 · Cited by 14
Li, Z., Zhao, M., Das, D., ZHAO, H., Ma, Y., Liu, W., Beaudouin-Lafon, M., Wang, F., Ramakrishnan, IV, & Bi, X. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517472 · Cited by 10
Modeling Gliding-based Target Selection for Blind Touchscreen Users
Ko, Y.-J., Putkonen, A., Aydin, A. S., Feiz, S., Wang, Y., Ashok, V., Ramakrishnan, IV, Oulasvirta, A., & Bi, X. (2021) · Proceedings of the 23rd International Conference on Mobile Human-Computer Interaction · doi:10.1145/3447526.3472022 · Cited by 3
Metadata
- Authors
- Holz, C., & Baudisch, P.
- Venue
- Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2011-05-07
- Pages
- 2501-2510
- OpenAlex
- W2295007882
- Cited by
- 277
- References
- 2
- FWCI
- 19.11
- Type
- conference paper
- Language
- en
- Open access
- closed
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