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Sven Mayer, Huy Viet Le, Niels Henze
Estimating the Finger Orientation on Capacitive Touchscreens Using Convolutional Neural Networks Proceedings of the 2017 ACM International Conference on Interactive Surfaces and Spaces (ISS '17), ACM, 2017-10-18 (bib) |
In the last years, touchscreens became the most common input device for a wide range of computers. While touchscreens are truly pervasive, commercial devices reduce the richness of touch input to two-dimensional positions on the screen. Recent work proposed interaction techniques to extend the richness of the input vocabulary using the finger orientation. Approaches for determining a finger's orientation using off-the-shelf capacitive touchscreens proposed in previous work already enable compelling use cases. However, the low estimation accuracy limits the usability and restricts the usage of finger orientation to non-precise input. With this paper, we provide a ground truth data set for capacitive touch screens recorded with a high-precision motion capture system. Using this data set, we show that a Convolutional Neural Network can outperform approaches proposed in previous work. Instead of relying on hand-crafted features, we trained the model based on the raw capacitive images. Thereby we reduce the pitch error by 9.8% and the yaw error by 45.7% |