Back-of-device interaction is a promising approach to interacting on smartphones. In this paper, we create a back-of-device command and text input technique called BackSwipe, which allows a user to hold a smartphone with one hand, and use the index finger of the same hand to draw a word-gesture anywhere at the back of the smartphone to enter commands and text. To support BackSwipe, we propose a back-of-device word-gesture decoding algorithm which infers the keyboard location from back-of-device gestures, and adjusts the keyboard size to suit the gesture scales; the inferred keyboard is then fed back into the system for decoding. Our user study shows BackSwipe is feasible and a promising input method, especially for command input in the one-hand holding posture: users can enter commands at an average accuracy of 92% with a speed of 5.32 seconds/command. The text entry performance varies across users. The average speed is 9.58 WPM with some users at 18.83 WPM; the average word error rate is 11.04% with some users at 2.85%. Overall, BackSwipe complements the extant smartphone interaction by leveraging the back of the device as a gestural input surface.

Entering commands on touchscreens can be noisy, but existing interfaces commonly adopt deterministic principles for deciding targets and often result in errors. Building on prior research of using Bayes' theorem to handle uncertainty in input, this paper formalized Bayes' theorem as a generic guiding principle for deciding targets in command input (referred to as "BayesianCommand"), developed three models for estimating prior and likelihood probabilities, and carried out experiments to demonstrate the effectiveness of this formalization. More specifically, we applied BayesianCommand to improve the input accuracy of (1) point-and-click and (2) word-gesture command input. Our evaluation showed that applying BayesianCommand reduced errors compared to using deterministic principles (by over 26.9% for point-and-click and by 39.9% for word-gesture command input) or applying the principle partially (by over 28.0% and 24.5%).

Correcting errors in entered text is a common task but usually diffcult to perform on mobile devices due to tedious cursor navigation steps. In this paper, we present JustCorrect, an intelligent post hoc text correction technique for smartphones. To make a correction, the user simply types the correct text at the end of their current input, and JustCorrect will automatically detect the error and apply the correction in the form of an insertion or a substitution. In this way, manual navigation steps are bypassed, and the correction can be committed with a single tap. We solved two critical problems to support JustCorrect: (1) Correction Algorithm: we propose an algorithm that infers the user's correction intention from the last typed word. (2) Input Modalities: our study revealed that both tap and gesture were suitable input modalities for performing JustCorrect. Based on our fndings, we integrated JustCorrect into a soft keyboard. Our user studies show that using JustCorrect reduces the text correction time by 12.8% over the stock Android keyboard and by 9.7% over the "Type, then Correct" text correction technique by Zhang et al. (2019). Overall, JustCorrect complements existing post hoc text correction techniques, making error correction more automatic and intelligent.

Entering text without having to pay attention to the keyboard is compelling but challenging due to the lack of visual guidance. We propose i'sFree to enable eyes-free gesture typing on a distant display from a touch-enabled remote control. i'sFree does not display the keyboard or gesture trace but decodes gestures drawn on the remote control into text according to an invisible and shifting Qwerty layout. i'sFree decodes gestures similar to a general gesture typing decoder, but learns from the instantaneous and historical input gestures to dynamically adjust the keyboard location. We designed it based on the understanding of how users perform eyes-free gesture typing. Our evaluation shows eyes-free gesture typing is feasible: reducing visual guidance on the distant display hardly affects the typing speed. Results also show that the i'sFree gesture decoding algorithm is effective, enabling an input speed of 23 WPM, 46% faster than the baseline eyes-free condition built on a general gesture decoder. Finally, i'sFree is easy to learn: participants reached 22 WPM in the first ten minutes, even though 40% of them were first-time gesture typing users.

Gesture typing has been a widely adopted text entry method on touchscreen devices. We have conducted a study to understand whether older adults could gesture type, how they type, what are the strengths and weaknesses of gesture typing, and how to further improve it. By logging stroke-level interaction data and leveraging the existing modeling tools, we compared the gesture and tap typing behavior of older adults with young adults. Our major finding is promising and encouraging. Gesture typing outperformed the typical tap typing for older adults, and was very easy for them to learn. The gesture typing input speed was 15.28% higher than that of tap typing for 14 older adults who had none gesture typing experience in the past. One of the main reasons was that older adults adopted the word-level inputting strategy in gesture typing, while often used the letter-level correction strategy in tap typing. Compared with young adults, older adults exhibited little degradation in gesture accuracy. Our study also led to implications on how to further improve gesture typing for older adults.

A virtual keyboard takes a large portion of precious screen real estate. We have investigated whether an invisible keyboard is a feasible design option, how to support it, and how well it performs. Our study showed users could correctly recall relative key positions even when keys were invisible, although with greater absolute errors and overlaps between neighboring keys. Our research also showed adapting the spatial model in decoding improved the invisible keyboard performance. This method increased the input speed by 11.5% over simply hiding the keyboard and using the default spatial model. Our 3-day multi-session user study showed typing on an invisible keyboard could reach a practical level of performance after only a few sessions of practice: the input speed increased from 31.3 WPM to 37.9 WPM after 20 - 25 minutes practice on each day in 3 days, approaching that of a regular visible keyboard (41.6 WPM). Overall, our investigation shows an invisible keyboard with adapted spatial model is a practical and promising interface option for the mobile text entry systems.

T9-like keyboards (i.e., 3×3 layouts) have been commonly used on small touchscreen devices to mitigate the problem of tapping tiny keys with imprecise finger touch (e.g., T9 is the default keyboard on Samsung Gear 2). In this paper, we proposed a computational approach to design optimal T9-like layouts by considering three key factors: clarity, speed, and learnability. In particular, we devised a clarity metric to model the word collisions (i.e., words with identical tapping sequences), used the Fitts-Digraph model to predict speed, and introduced a Qwerty-bounded constraint to ensure high learnability. Founded upon rigorous mathematical optimization, our investigation led to Optimal-T9, an optimized T9-like layout which outperformed the original T9 and other T9-like layouts. A user study showed that its average input speed was 17% faster than T9 and 26% faster than a T9-like layout from literature. Optimal-T9 also drastically reduced the error rate by 72% over a regular Qwerty keyboard. Subjective ratings were in favor of Optimal-T9: it had the lowest physical, mental demands, and the best perceived-performance among all the tested keyboards. Overall, our investigation has led to a more efficient, and more accurate T9-like layout than the original T9. Such a layout would immediately benefit both T9-like keyboard users and small touchscreen device users.

Smartphones are now a central technology in the daily lives of billions, but it relies on its battery to perform. Battery optimization is thereby a crucial design constraint in any mobile OS and device. However, even with new low-power methods, the ever-growing touchscreen remains the most power-hungry component. We propose an Ultra-Low-Power Mode (ULPM) for mobile devices that allows for touch interaction without visual feedback and exhibits significant power savings of up to 60% while allowing to complete interactive tasks. We demonstrate the effectiveness of the screenless ULPM in text-entry tasks, camera usage, and listening to videos, showing only a small decrease in usability for typical users.

Redirected walking techniques can enhance the immersion and visual-vestibular comfort of virtual reality (VR) navigation, but are often limited by the size, shape, and content of the physical environments. We propose a redirected walking technique that can apply to small physical environments with static or dynamic obstacles. Via a head- and eye-tracking VR headset, our method detects saccadic suppression and redirects the users during the resulting temporary blindness. Our dynamic path planning runs in real-time on a GPU, and thus can avoid static and dynamic obstacles, including walls, furniture, and other VR users sharing the same physical space. To further enhance saccadic redirection, we propose subtle gaze direction methods tailored for VR perception. We demonstrate that saccades can significantly increase the rotation gains during redirection without introducing visual distortions or simulator sickness. This allows our method to apply to large open virtual spaces and small physical environments for room-scale VR. We evaluate our system via numerical simulations and real user studies.

Without violating existing app security enforcement, malicious modules inside apps, such as a library or an external class, can steal private data and abuse sensitive capabilities meant for other modules inside the same apps. These so-called "module-level attacks" are quickly emerging, fueled by the pervasive use of third-party code in apps and the lack of module-level security enforcement on mobile platforms.