Browsing by Author "Feit, Anna Maria"
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- AdaM: Adapting Multi-User Interfaces for Collaborative Environments in Real-Time
A4 Artikkeli konferenssijulkaisussa(2018-04) Park, Seonwook; Gebhardt, Christoph; Rädle, Roman; Feit, Anna Maria; Vrzakova, Hana; Dayama, Niraj Ramesh; Yeo, Hui-Shyong; Klokmose, Clemens N; Quigley, Aaron; Oulasvirta, AnttiDeveloping cross-device multi-user interfaces (UIs) is a challenging problem. There are numerous ways in which content and interactivity can be distributed. However, good solutions must consider multiple users, their roles, their preferences and access rights, as well as device capabilities. Manual and rule-based solutions are tedious to create and do not scale to larger problems nor do they adapt to dynamic changes, such as users leaving or joining an activity. In this paper, we cast the problem of UI distribution as an assignment problem and propose to solve it using combinatorial optimization. We present a mixed integer programming formulation which allows real-time applications in dynamically changing collaborative settings. It optimizes the allocation of UI elements based on device capabilities, user roles, preferences, and access rights. We present a proof-of-concept designer-in-the-loop tool, allowing for quick solution exploration. Finally, we compare our approach to traditional paper prototyping in a lab study. - Assignment Problems for Optimizing Text Input
School of Electrical Engineering | Doctoral dissertation (article-based)(2018) Feit, Anna MariaText 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. - AUIT - the Adaptive User Interfaces Toolkit for Designing XR Applications
A4 Artikkeli konferenssijulkaisussa(2022-10-29) Evangelista Belo, João Marcelo; Lystbæk, Mathias N.; Feit, Anna Maria; Pfeuffer, Ken; Kán, Peter; Oulasvirta, Antti; Grønbæk, KajAdaptive 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. - How do people type on mobile devices? Observations from a study with 37,000 volunteers
A4 Artikkeli konferenssijulkaisussa(2019-10-01) Palin, Kseniia; Feit, Anna Maria; Kim, Sunjun; Kristensson, Per Ola; Oulasvirta, AnttiThis paper presents a large-scale dataset on mobile text entry collected via a web-based transcription task performed by 37,370 volunteers. The average typing speed was 36.2 WPM with 2.3% uncorrected errors. The scale of the data enables powerful statistical analyses on the correlation between typing performance and various factors, such as demographics, finger usage, and use of intelligent text entry techniques. We report effects of age and finger usage on performance that correspond to previous studies. We also find evidence of relationships between performance and use of intelligent text entry techniques: auto-correct usage correlates positively with entry rates, whereas word prediction usage has a negative correlation. To aid further work on modeling, machine learning and design improvements in mobile text entry, we make the code and dataset openly available. - Observations on typing from 136 million keystrokes
A4 Artikkeli konferenssijulkaisussa(2018-04-20) Dhakal, Vivek; Feit, Anna Maria; Kristensson, Per Ola; Oulasvirta, AnttiWe 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. - Physical keyboards in Virtual reality: Analysis of typing performance and effects of avatar hands
A4 Artikkeli konferenssijulkaisussa(2018-04-20) Knierim, Pascal; Schwind, Valentin; Feit, Anna Maria; Nieuwenhuizen, Florian; Henze, NielsEntering 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. - Selection-based text entry In Virtual Reality
A4 Artikkeli konferenssijulkaisussa(2018-04-20) Speicher, Marco; Feit, Anna Maria; Ziegler, Pascal; Krüger, AntonioIn recent years, Virtual Reality (VR) and 3D User Interfaces (3DUI) have seen a drastic increase in popularity, especially in terms of consumer-ready hardware and software. While the technology for input as well as output devices is market ready, only a few solutions for text input exist, and empirical knowledge about performance and user preferences is lacking. In this paper, we study text entry in VR by selecting characters on a virtual keyboard. We discuss the design space for assessing selection-based text entry in VR. Then, we implement six methods that span different parts of the design space and evaluate their performance and user preferences. Our results show that pointing using tracked hand-held controllers outperforms all other methods. Other methods such as head pointing can be viable alternatives depending on available resources. We summarize our findings by formulating guidelines for choosing optimal virtual keyboard text entry methods in VR.