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Today, touchscreens stand as the most prevalent input devices of mobile computing devices (smartphones, tablets, smartwatches). Yet, compared with desktop or laptop computers, the limited shortcut keys and physical buttons on touchscreen devices, coupled with the fat finger problem, often lead to slower and more error-prone input and navigation, especially when dealing with text editing and other complex interaction tasks. We introduce an innovative gesture set based on finger rotations in the yaw, pitch, and roll directions on a touchscreen, diverging significantly from traditional two-dimensional interactions and promising to expand the gesture library. Despite active research in estimation of finger angles, however, the previous work faces substantial challenges, including significant estimation errors and unstable sequential outputs. Variability in user behavior further complicates the isolation of movements to a single rotational axis, leading to accidental disturbances and screen coordinate shifts that interfere with the existing sliding gestures. Consequently, the direct application of finger angle estimation algorithms for recognizing three-dimensional rotational gestures is impractical. SwivelTouch leverages the analysis of finger movement characteristics on the touchscreen captured through original capacitive image sequences, which aims to rapidly and accurately identify these advanced 3D gestures, clearly differentiating them from conventional touch interactions like tapping and sliding, thus enhancing user interaction with touch devices and meanwhile compatible with existing 2D gestures. User study further confirms that the implementation of SwivelTouch significantly enhances the efficiency of text editing on smartphones.
Li et al. (Mon,) studied this question.