ATK: Enabling Ten-Finger Freehand Typing in Air Based on 3D Hand Tracking Data. Yi, X., Yu, C., Zhang, M., Gao, S., Sun, K., & Shi, Y. In Proceedings of the ACM Symposium on User Interface Software and Technology (UIST), of UIST '15, pages 539-548, 11, 2015. ACM. Website abstract bibtex Ten-finger freehand mid-air typing is a potential solution for post-desktop interaction. However, the absence of tactile feedback as well as the inability to accurately distinguish tapping finger or target keys exists as the major challenge for mid-air typing. In this paper, we present ATK, a novel interaction technique that enables freehand ten-finger typing in the air based on 3D hand tracking data. Our hypothesis is that expert typists are able to transfer their typing ability from physical keyboards to mid-air typing. We followed an iterative approach in designing ATK. We first empirically investigated users' mid-air typing behavior, and examined fingertip kinematics during tapping, correlated movement among fingers and 3D distribution of tapping endpoints. Based on the findings, we proposed a probabilistic tap detection algorithm, and augmented Goodman's input correction model to account for the ambiguity in distinguishing tapping finger. We finally evaluated the performance of ATK with a 4-block study. Participants typed 23.0 WPM with an uncorrected word-level error rate of 0.3% in the first block, and later achieved 29.2 WPM in the last block without sacrificing accuracy.
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title = {ATK: Enabling Ten-Finger Freehand Typing in Air Based on 3D Hand Tracking Data},
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abstract = {Ten-finger freehand mid-air typing is a potential solution for post-desktop interaction. However, the absence of tactile feedback as well as the inability to accurately distinguish tapping finger or target keys exists as the major challenge for mid-air typing. In this paper, we present ATK, a novel interaction technique that enables freehand ten-finger typing in the air based on 3D hand tracking data. Our hypothesis is that expert typists are able to transfer their typing ability from physical keyboards to mid-air typing. We followed an iterative approach in designing ATK. We first empirically investigated users' mid-air typing behavior, and examined fingertip kinematics during tapping, correlated movement among fingers and 3D distribution of tapping endpoints. Based on the findings, we proposed a probabilistic tap detection algorithm, and augmented Goodman's input correction model to account for the ambiguity in distinguishing tapping finger. We finally evaluated the performance of ATK with a 4-block study. Participants typed 23.0 WPM with an uncorrected word-level error rate of 0.3% in the first block, and later achieved 29.2 WPM in the last block without sacrificing accuracy.},
bibtype = {inProceedings},
author = {Yi, Xin and Yu, Chun and Zhang, Mingrui and Gao, Sida and Sun, Ke and Shi, Yuanchun},
booktitle = {Proceedings of the ACM Symposium on User Interface Software and Technology (UIST)}
}
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