Anna Maria Feit

Investigating the Dexterity of Multi-Finger Input for Mid-Air Text Entry

This paper investigates an emerging input method enabled by progress in hand tracking: input by free motion of fingers. The method is expressive, potentially fast, and usable across many settings as it does not insist on physical contact or visual feedback. Our goal is to inform the design of high-performance input methods by providing detailed analysis of the performance and anatomical characteristics of finger motion. We conducted an experiment using a commercially available sensor to report on the speed, accuracy, individuation, movement ranges, and individual differences of each finger. Findings show differences of up to 50% in movement times and provide indices quantifying the individuation of single fingers. We apply our findings to text entry by computational optimization of multi-finger gestures in mid-air. To this end, we define a novel objective function that considers performance, anatomical factors, and learnability. First investigations of one optimization case show entry rates of 22 words per minute (WPM). We conclude with a critical discussion of the limitations posed by human factors and performance characteristics of existing markerless hand trackers.

How We Type: Movement Strategies and Performance in Everyday Typing

This paper revisits the present understanding of typing, which originates mostly from studies of trained typists using the ten-finger touch typing system. Our goal is to characterise the majority of present-day users who are untrained and employ diverse, self-taught techniques. In a transcription task, we compare self-taught typists and those that took a touch typing course. We report several differences in performance, gaze deployment and movement strategies. The most surprising finding is that self-taught typists can achieve performance levels comparable with touch typists, even when using fewer fingers. Motion capture data exposes 3 predictors of high performance: 1) unambiguous mapping (a letter is consistently pressed by the same finger), 2) active preparation of upcoming keystrokes, and 3) minimal global hand motion. We release an extensive dataset on everyday typing behavior.

Physical Keyboards in Virtual Reality: Analysis of Typing Performance and Effects of Avatar Hands

Entering text is one of the most common tasks when interacting with computing systems. Virtual Reality (VR) presents a challenge as neither the users 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 users 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

In 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.

PianoText: transferring musical expertise to text entry

We present PianoText, a text entry method based on a piano keyboard with an optimized mapping between notes and chords of music to letters of the English language. PianoText exemplifies the idea of transferring musical expertise to a text entry task by computationally searching for mappings between frequent motor patterns while considering their n-gram frequency distributions and respecting constraints affecting the playability of music. In the Interactivity session, audience members with piano skills can transcribe text with PianoText, and a trained pianist will show that it allows him to generate text at speeds close to that of professional QWERTY-typists.

Observations on Typing from 136 Million Keystrokes

We 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 that are 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 sur- prisingly 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.

AdaM: Adapting Multi-User Interfaces for Collaborative Environments in Real-Time

Developing 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.

Computational Design of Input Methods

Designing a user interface or input method requires to evaluate and trade-off many criteria. The corresponding design spaces are huge, making it impossible to consider every potential design. Therefore, my work focuses on the use of computational methods for the design of input methods. I follow a modelling-optimization approach: understand and model the characteristics of the interaction, formulate the design space and develop (multi-) objective functions to evaluate designs, and develop algorithms to systematically search for the best design. In my projects I applied this approach to develop better text entry methods. Among others, I modelled the performance and anatomical constraints of the hand to computationally optimize multi-finger gestures for mid-air input, and studied how people type on physical keyboards, in order to understand and model the performance of two-hand typing.

Computational Support for Functionality Selection in Interaction Design

Designing interactive technology entails several objectives, one of which is identifying and selecting appropriate functionality. Given candidate functionalities such as “print,” “bookmark,” and “share,” a designer has to choose which functionalities to include and which to leave out. Such choices critically affect the acceptability, productivity, usability, and experience of the design. However, designers may overlook reasonable designs because there is an exponential number of functionality sets and multiple factors to consider. This article is the first to formally define this problem and propose an algorithmic method to support designers to explore alternative functionality sets in early stage design. Based on interviews of professional designers, we mathematically define the task of identifying functionality sets that strike the best balance among four objectives: usefulness, satisfaction, ease of use, and profitability. We develop an integer linear programming solution that can efficiently solve very large instances (set size over 1,300) on a regular computer. Further, we build on techniques of robust optimization to search for diverse and surprising functionality designs. Empirical results from a controlled study and field deployment are encouraging. Most designers rated computationally created sets to be of the comparable or superior quality than their own. Designers reported gaining better understanding of available functionalities and the design space.