UC Merced

Text and numeric input and editing are prevalent in our daily digital interactions. Although text entry has been meticulously investigated by the human-computer interaction (HCI) community, numeric input and editing have not been as thoroughly examined. Consequently, the methods employed on mobile and wearable devices, such as smartwatches, are inefficient compared to their desktop counterparts. This inefficiency is primarily due to the smaller screen real estate of these devices, which cannot display all relevant information, the absence of haptic feedback provided by physical keyboards and keypads, and an excessive reliance on precise target selection, which is difficult on smaller screens. This dissertation investigates, designs, and evaluates novel methods for text and numeric input and editing for mobile and wearable devices by leveraging the existing capabilities of smartphones and smartwatches. The first part of this dissertation proposes three new numeric input and editing methods for smartwatches based on gestures, tilting, and force. The proposed methods enable users to actively switch between slow-and-steady and fast-and-continuous increments and decrements of numeric values during the input process. Results reveal that the gesture-based method yields a significantly faster input rate and is perceived as faster, more accurate, and the least mentally and physically demanding compared to the other methods. The second part explores the possibility of using a force-based approach to target selection on smartwatches and its use in character entry. The initial study identifies the most comfortable range of force on smartwatches. The subsequent study compares the performance of tap and force-tap in a Fitts' Law setting. Results revealed that force-tap is significantly better at selecting smaller targets, while tap outperforms force-tap for larger targets. We then develop two new force-based keyboards to demonstrate the feasibility of force input in practical scenarios. These single-row alphabetical keyboards enable character-level text entry by performing slides and varying contact force. In a user study, these keyboards yield about 4 wpm with about a 2% error rate, demonstrating the viability of force input on smaller screens. The third part of the dissertation presents GeShort, a novel method for one-handed text editing and formatting on mobile devices. It uses simple rules to facilitate direct cursor positioning, gestural shortcuts inspired by keyboard hotkeys for editing and formatting, and a floating clipboard to enable delayed, repeated, and block editing. A comparison between GeShort and the default Google keyboard reveals that users perform editing and formatting tasks about 11% and 22% faster, respectively, with GeShort, achieved by significantly reducing selection time by 11% and action time by 17%. A second study comparing the clipboard features of the two methods revealed that users perform advanced editing tasks 34% faster with GeShort. The findings presented in this dissertation illustrate that numeric and character entry and editing can be significantly improved in terms of speed, accuracy, and user preference. This is achieved by employing careful interface and interaction design, utilizing simple rules, and applying language models, all while making use of the existing capabilities of mobile and wearable devices. Importantly, this enhancement does not require the addition of extra sensors, new hardware, or the computational power necessary for complex learning models.