![]() ![]() In these situations we facilitate interaction by 1) showing the visual feedback of the hand hover near user's gaze point and 2) decrease the requisite of positional accuracy by employing gestural information. In contrast, above-surface sensing and eye tracking provides information about how user's hands and gaze are distributed across the interface. Conventional touch input relies on positional accuracy, thereby requiring tight visual monitoring of one's own motor action. We explore the combination of above-surface sensing with eye tracking to facilitate concurrent interaction with multiple regions on touch screens. Participants' subjective feedback also indicated that TOAST offered a natural and efficient typing experience. And with less than 10 minutes of practice, they reached 44.6 WPM without sacrificing accuracy. Participants reached a pick-up speed of 41.4 WPM with a character-level error rate of 0.6%. 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. 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%. 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. 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. ![]() Through a user study, we then examined users' eyes-free touch typing behavior on an interactive tabletop with only asterisk feedback. location and size) estimation based on users' typing data. We first formalized the problem of keyboard parameter (e.g. In this paper, we present TOAST, an eyes-free keyboard technique for enabling efficient touch typing on touch-sensitive surfaces. interactive tabletop) is challenging due to lack of tactile feedback and hand drifting. Our experimental studies yielded the following results: 1) Most of the QWERTY-familiar typists who have varying typing habits were easily adaptable to the proposed keyboard design and 2) The proposed keyboard outperformed existing virtual keyboards in terms of typing speed and several error rates, and eventually achieved a typing speed of approximately 56 WPM. Based on these two techniques, we implemented a novel hand pose aware virtual keyboard that is tolerant of hand drift. Thus, rather than locating the touch point as in the case of existing virtual keyboards, we attempted to use unique hand poses to infer the target key. Secondly, our investigation of skilled typists demonstrated that hand poses vary when the typists touch different keys. Firstly, as most typists enter the keys in the same column with a predetermined finger only, we restricted these keys to be typed by their corresponding fingers. To overcome this, we proposed to utilize the typing patterns of skilled typists. An unintentional hand drift adversely affects the typing performance of conventional virtual keyboards. ![]()
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