The movement of a user's face, easily detected by a smartphone's front camera, is an underexploited input modality for mobile interactions. We introduce three sets of face-engaged interaction techniques for augmenting the traditional mobile inputs, which leverages the combination of the head movements with touch gestures and device motions, all sensed via the phone's built-in sensors. We systematically present the space of design considerations for mobile interactions using one or more of the three input modalities (i.e., touch, motion, and head). The additional affordances of the proposed techniques expand the mobile interaction vocabulary, and can facilitate unique usage scenarios such as one-hand or touch-free interaction. An initial evaluation was conducted and users had positive reactions to the new techniques, indicating the promise of an intuitive and convenient user experience.
For touch only input techniques, one of the major issues is the imprecision and occlusion of touch interfaces, also known as the “fat finger” problem. For example, Offset Cursor displays the cursor at a fixed distance on the screen to avoid the finger occlusion @cite_20 ; similarly, Shift applies a hybrid approach where a normal coarse direct touch selection is followed by a precise position adjustment @cite_10 . Along this line, Roudaut al proposed TapTap and MagStick that further improve the efficiency and accuracy of small target selection @cite_25 . Another relevant area is that concerning one-handed interaction. Karlson al have shown many cases in which users prefer one-handed use for mobile devices @cite_11 . Some gestures for performing one-handed zooming have been widely used on commercial systems, such as “double tap” on iOS and “tap then slide” in Google Map mobile app. Other techniques such as “rubbing” @cite_5 or “rolling” @cite_30 have also been proposed to facilitate single hand touch manipulations.
@cite_30 @cite_5 @cite_10 @cite_25 @cite_20 @cite_11
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We introduce two families of techniques, rubbing and tapping, that use zooming to make possible precise interaction on passive touch screens, and describe examples of each. Rub-Pointing uses a diagonal rubbing gesture to integrate pointing and zooming in a single-handed technique. In contrast, Zoom-Tapping is a two-handed technique in which the dominant hand points, while the non-dominant hand taps to zoom, simulating multi-touch functionality on a single-touch display. Rub-Tapping is a hybrid technique that integrates rubbing with the dominant hand to point and zoom, and tapping with the non-dominant hand to confirm selection. We describe the results of a formal user study comparing these techniques with each other and with the well-known Take-Off and Zoom-Pointing selection techniques. Rub-Pointing and Zoom-Tapping had significantly fewer errors than Take-Off for small targets, and were significantly faster than Take-Off and Zoom-Pointing. We show how the techniques can be used for fluid interaction in an image viewer and in Google Maps. We present the design and evaluation of TapTap and MagStick, two thumb interaction techniques for target acquisition on mobile devices with small touch-screens. These two techniques address all the issues raised by the selection of targets with the thumb on small tactile screens: screen accessibility, visual occlusion and accuracy. A controlled experiment shows that TapTap and MagStick allow the selection of targets in all areas of the screen in a fast and accurate way. They were found to be faster than four previous techniques except Direct Touch which, although faster, is too error prone. They also provided the best error rate of all tested techniques. Finally the paper also provides a comprehensive study of various techniques for thumb based touch-screen target selection. Bare hand pointing on touch screens both benefits and suffers from the nature of direct input. This work explores techniques to overcome its limitations. Our goal is to design interaction tools allowing pixel level pointing in a fast and efficient manner. Based on several cycles of iterative design and testing, we propose two techniques: Cross-Keys that uses discrete taps on virtual keys integrated with a crosshair cursor, and an analog Precision-Handle that uses a leverage (gain) effect to amplify movement precision from the user's finger tip to the end cursor. We conducted a formal experiment with these two techniques, in addition to the previously known Zoom-Pointing and Take-Off as baseline anchors. Both subjective and performance measurements indicate that Precision-Handle and Cross-Keys complement existing techniques for touch screen interaction. Touch typing on flat surfaces (e.g. interactive tabletop) is challenging due to lack of tactile feedback and hand drifting. In this paper, we present TOAST, an eyes-free keyboard technique for enabling efficient touch typing on touch-sensitive surfaces. We first formalized the problem of keyboard parameter (e.g. location and size) estimation based on users' typing data. Through a user study, we then examined users' eyes-free touch typing behavior on an interactive tabletop with only asterisk feedback. We fitted the keyboard model to the typing data, results suggested that the model parameters (keyboard location and size) changed not only between different users, but also within the same user along with time. Based on the results, we proposed a Markov-Bayesian algorithm for input prediction, which considers the relative location between successive touch points within each hand respectively. Simulation results showed that based on the pooled data from all users, this model improved the top-1 accuracy of the classical statistical decoding algorithm from 86.2 to 92.1 . In a second user study, we further improved TOAST with dynamical model parameter adaptation, and evaluated users' text entry performance with TOAST using realistic text entry tasks. Participants reached a pick-up speed of 41.4 WPM with a character-level error rate of 0.6 . And with less than 10 minutes of practice, they reached 44.6 WPM without sacrificing accuracy. Participants' subjective feedback also indicated that TOAST offered a natural and efficient typing experience.