Assignment Problems for Optimizing Text Input

Text input methods are an integral part of our daily interaction with digital devices. However, their design poses a complex problem: for any method, we must decide which input action (a button press, a hand gesture, etc.) produces which symbol (e.g., a character or word). With only 26 symbols and input actions, there are already more than 10^26 distinct solutions, making it impossible to find the best one through manual design. Prior work has shown that we can use optimization methods to search such large design spaces efficiently and automatically find the best solution for a given task and objective. However, work in this domain has been limited mostly to the performance optimization of keyboards. The Ph.D. thesis advances the field of text-entry optimization by enlarging the space of optimizable text-input methods and proposing new criteria for assessing their optimality. Firstly, the design problem is formulated as an assignment problem for integer programming. This enables the use of standard mathematical solvers and algorithms for efficiently finding good solutions. Then, objective functions are developed, for assessing their optimality with respect to motor performance, ergonomics, and learnability. The corresponding models extend beyond interaction with soft keyboards, to consider multi-finger input, novel sensors, and alternative form factors. In addition, the thesis illustrates how to formulate models from prior work in terms of an assignment problem, providing a coherent theoretical basis for text-entry optimization. The proposed objectives are applied in the optimization of three assignment problems: text input with multi-finger gestures in mid-air, text input on a long piano keyboard, and -- for a contribution to the official French keyboard standard -- input of special characters via a physical keyboard. Combining the proposed models offers a multi-objective optimization approach able to capture the complex cognitive and motor processes during typing. Finally, the dissertation discusses future work that is needed to solve the long-standing problem of finding the optimal layout for physical keyboards, in light of empirical evidence that prior models are insufficient to respond to the diverse typing strategies people employ with modern keyboards. The thesis advances the state of the art in text-entry optimization by proposing novel objective functions that quantify the performance, ergonomics and learnabilityof a text input method. The objectives presented are formulated as assignment problems, which can be solved with integer programming via standard mathematical solvers or heuristic algorithms. While the work focused on text input, the assignment problem can be used to model other design problems in HCI (e.g., how best to assign commands to UI controls or distribute UI elements across several devices), for which the same problem formulations, optimization techniques, and even models could be applied

Typing Behavior is About More than Speed: Users' Strategies for Choosing Word Suggestions Despite Slower Typing Rates

Mobile word suggestions can slow down typing, yet are still widely used. To investigate the apparent benefits beyond speed, we analyzed typing behavior of 15,162 users of mobile devices. Controlling for natural typing speed (a confounding factor not considered by prior work), we statistically show that slower typists use suggestions more often but are slowed down by doing so. To better understand how these typists leverage suggestions - if not to improve their speed - we extract eight usage strategies, including completion, correction, and next-word prediction. We find that word characteristics, such as length or frequency, along with the strategy, are predictive of whether a user will select a suggestion. We show how to operationalize our findings by building and evaluating a predictive model of suggestion selection. Such a model could be used to augment existing suggestion algorithms to consider people's strategic use of word predictions beyond speed and keystroke savings.ISSN:2573-014

Complex Interaction as Emergent Behaviour: Simulating Mid-Air Virtual Keyboard Typing using Reinforcement Learning.

Accurately modelling user behaviour has the potential to significantly improve the quality of human-computer interaction. Traditionally, these models are carefully hand-crafted to approximate specific aspects of well-documented user behaviour. This limits their availability in virtual and augmented reality where user behaviour is often not yet well understood. Recent efforts have demonstrated that reinforcement learning can approximate human behaviour during simple goal-oriented reaching tasks. We build on these efforts and demonstrate that reinforcement learning can also approximate user behaviour in a complex mid-air interaction task: typing on a virtual keyboard. We present the first reinforcement learning-based user model for mid-air and surface-aligned typing on a virtual keyboard. Our model is shown to replicate high-level human typing behaviour. We demonstrate that this approach may be used to augment or replace human testing during the validation and development of virtual keyboards

Observations on typing from 136 million keystrokes

| openaire: EC/H2020/637991/EU//COMPUTEDWe report on typing behaviour and performance of 168,000 volunteers in an online study. The large dataset allows detailed statistical analyses of keystroking patterns, linking them to typing performance. Besides reporting distributions and confirming some earlier findings, we report two new findings. First, letter pairs typed by different hands or fingers are more predictive of typing speed than, for example, letter repetitions. Second, rollover-typing, wherein the next key is pressed before the previous one is released, is surprisingly prevalent. Notwithstanding considerable variation in typing patterns, unsupervised clustering using normalised inter-key intervals reveals that most users can be divided into eight groups of typists that differ in performance, accuracy, hand and finger usage, and rollover. The code and dataset are released for scientific use.Peer reviewe

Physical keyboards in Virtual reality

Entering text is one of the most common tasks when interacting with computing systems. Virtual Reality (VR) presents a challenge as neither the user's hands nor the physical input devices are directly visible. Hence, conventional desktop peripherals are very slow, imprecise, and cumbersome. We developed a apparatus that tracks the user's hands, and a physical keyboard, and visualize them in VR. In a text input study with 32 participants, we investigated the achievable text entry speed and the effect of hand representations and transparency on typing performance, workload, and presence. With our apparatus, experienced typists benefited from seeing their hands, and reach almost outside-VR performance. Inexperienced typists profited from semi-transparent hands, which enabled them to type just 5.6 WPM slower than with a regular desktop setup. We conclude that optimizing the visualization of hands in VR is important, especially for inexperienced typists, to enable a high typing performance.Peer reviewe

XRgonomics: Facilitating the Creation of Ergonomic 3D Interfaces

Arm discomfort is a common issue in Cross Reality applications involving prolonged mid-air interaction. Solving this problem is difficult because of the lack of tools and guidelines for 3D user interface design. Therefore, we propose a method to make existing ergonomic metrics available to creators during design by estimating the interaction cost at each reachable position in the user's environment. We present XRgonomics, a toolkit to visualize the interaction cost and make it available at runtime, allowing creators to identify UI positions that optimize users' comfort. Two scenarios show how the toolkit can support 3D UI design and dynamic adaptation of UIs based on spatial constraints. We present results from a walkthrough demonstration, which highlight the potential of XRgonomics to make ergonomics metrics accessible during the design and development of 3D UIs. Finally, we discuss how the toolkit may address design goals beyond ergonomics

AUIT - the Adaptive User Interfaces Toolkit for Designing XR Applications

Funding Information: This work was supported by the Innovation Fund Denmark (IFD grant no. 6151-00006B) as part of the Manufacturing Academy of Denmark (MADE) Digital project. Antti Oulasvirta was supported by the Finnish Center for Artifcial Intelligence (FCAI), and Academy of Finland grants ‘Human Automata’ and ‘BAD’. Special thanks to Aïna Linn Georges for the help with revisions and the anonymous reviewers for constructive feedback that helped improve the paper. Publisher Copyright: © 2022 Owner/Author.Adaptive user interfaces can improve experiences in Extended Reality (XR) applications by adapting interface elements according to the user's context. Although extensive work explores different adaptation policies, XR creators often struggle with their implementation, which involves laborious manual scripting. The few available tools are underdeveloped for realistic XR settings where it is often necessary to consider conflicting aspects that affect an adaptation. We fill this gap by presenting AUIT, a toolkit that facilitates the design of optimization-based adaptation policies. AUIT allows creators to flexibly combine policies that address common objectives in XR applications, such as element reachability, visibility, and consistency. Instead of using rules or scripts, specifying adaptation policies via adaptation objectives simplifies the design process and enables creative exploration of adaptations. After creators decide which adaptation objectives to use, a multi-objective solver finds appropriate adaptations in real-time. A study showed that AUIT allowed creators of XR applications to quickly and easily create high-quality adaptations.Peer reviewe