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We compared three common usability testing methods—Concurrent Think-Aloud, Retrospective Think-Aloud and Co-discovery—with frail older adults. We found that Co-discovery is the most effective method for this group. Additionally, we interviewed Human-Computer Interaction experts who work with older adults. These experts discussed, for instance, the importance of leveraging usability tests to enhance participant motivation to engage with technology. We consolidated our findings from the usability studies with older adults and from interviews with experts to create a set of recommendations for performing usability tests with frail older adults. One of the core recommendations is to enhance participants’ sense of autonomy and self-confidence during usability tests.

We present the design and implementation of a word-level stroking system called Fisch, which is intended to improve the speed of character-level unistrokes. Importantly, Fisch does not alter the way in which character-level unistrokes are made, but allows users to gradually ramp up to word-level unistrokes by extending their letters in minimal ways. Fisch relies on in-stroke word completion, a flexible design for fluidly turning unistroke letters into whole words. Fisch can be memorized at the motor level since word completions always appear at the same positions relative to the strokes being made. Our design for Fisch is suitable for use with any unistroke alphabet. We have implemented Fisch for multiple versions of EdgeWrite, and results show that Fisch reduces the number of strokes during entry by 43.9% while increasing the rate of entry. An informal test of "record speed" with the stylus version resulted in 50-60 wpm with no uncorrected errors.

Data not suitable for classic parametric statistical analyses arise frequently in human–computer interaction studies. Various nonparametric statistical procedures are appropriate and advantageous when used properly. This chapter organizes and illustrates multiple nonparametric procedures, contrasting them with their parametric counterparts. Guidance is given for when to use nonparametric analyses and how to interpret and report their results.

With the ubiquity of mobile touchscreen devices like smartphones, two widely used text entry methods have emerged: small touch-based keyboards and speech recognition. Although speech recognition has been available on desktop computers for years, it has continued to improve at a rapid pace, and it is currently unknown how today's modern speech recognizers compare to state-of-the-art mobile touch keyboards, which also have improved considerably since their inception. To discover both methods' "upper-bound performance," we evaluated them in English and Mandarin Chinese on an Apple iPhone 6 Plus in a laboratory setting. Our experiment was carried out using Baidu's Deep Speech 2, a deep learning-based speech recognition system, and the built-in Qwerty (English) or Pinyin (Mandarin) Apple iOS keyboards. We found that with speech recognition, the English input rate was 2.93 times faster (153 vs. 52 WPM), and the Mandarin Chinese input rate was 2.87 times faster (123 vs. 43 WPM) than the keyboard for short message transcription under laboratory conditions for both methods. Furthermore, although speech made fewer errors during entry (5.30% vs. 11.22% corrected error rate), it left slightly more errors in the final transcribed text (1.30% vs. 0.79% uncorrected error rate). Our results show that comparatively, under ideal conditions for both methods, upper-bound speech recognition performance has greatly improved compared to prior systems, and might see greater uptake in the future, although further study is required to quantify performance in non-laboratory settings for both methods., Comment: 23 pages

The computing literature often draws a sharp distinction between input and output; computer scientists are used to regarding a screen as a passive output device and a mouse as a pure input device. However, nearly all examples of human-computer interaction require both input and output to do anything useful. For example, what good would a mouse be without the corresponding feedback embodied by the cursor on the screen, as well as the sound and feel of the buttons when they are clicked? The distinction between output devices and input devices becomes even more blurred in the real world. A sheet of paper can be used to both record ideas (input) and display them (output). Clay reacts to the sculptor’s fingers yet also provides feedback through the curvature and texture of its surface. Indeed, the complete and seamless integration of input and output is becoming a common research theme in advanced computer interfaces such as ubiquitous computing (Weiser, 1991) and tangible interaction (Ishii & Ullmer, 1997). Input and output bridge the chasm between a computer’s inner world of bits, and the real world perceptible to the human senses. Input to computers consists of sensed information about the physical environment. Familiar examples include the mouse, which senses movement across a surface, and the keyboard, which detects a contact closure when the user presses a key. However, any sensed information about physical properties of people, places, or things can serve as input to computer systems. Output from computers can comprise any emission or modification to the physical environment, such as a display (including the cathode ray tube (CRT), flat-panel displays, or even light emitting diodes), speakers, or tactile and force feedback devices (sometimes referred to as haptic displays). An interaction technique is the fusion of input and output, consisting of all hardware and software elements, that provides a way for the user to accomplish a low-level task. For example, in the traditional graphical user interface, users can scroll through a document by clicking or dragging the mouse (input) within a scroll bar displayed on the screen (output). Input and output bridge the chasm between a computer’s inner world of bits, and the real world perceptible to the human senses. Input to computers consists of sensed information about the physical environment. Familiar examples include the mouse, which senses movement across a surface, and the keyboard, which detects a contact closure when the user presses a key. However, any sensed information about physical properties of people, places, or things can serve as input to computer systems. Output from computers can comprise any emission or modification to the physical environment, such as a display (including the cathode ray tube (CRT), flat-panel displays, or even light emitting diodes), speakers, or tactile and force feedback devices (sometimes referred to as haptic displays). An interaction technique is the fusion of input and output, consisting of all hardware and software elements, that provides a way for the user to accomplish a low-level task. For example, in the traditional graphical user interface, users can scroll through a document by clicking or dragging the mouse (input) within a scroll bar displayed on the screen (output).

Though exciting scientifically, autonomous agent design can result in legal liability. This paper surveys those legal concerns, focusing on issues arising from the unique qualities of agents not found in conventional software. Informed designers can more effectively reduce their liability exposure and influence emerging agent liability law and policies.

Pointing to targets in graphical user interfaces remains a frequent and fundamental necessity in modern computing systems. Yet for millions of people with motor impairments, children, and older users, pointing—whether with a mouse cursor, a stylus, or a finger on a touch screen—remains a major access barrier because of the fine-motor skills required. In a series of projects inspired by and contributing to ability-based design, we have reconsidered the nature and assumptions behind pointing, resulting in changes to how mouse cursors work, the types of targets used, the way interfaces are designed and laid out, and even how input devices are used. The results from these explorations show that people with motor difficulties can acquire targets in graphical user interfaces when interfaces are designed to better match the abilities of their users. Ability-based design, as both a design philosophy and a design approach, provides a route to realizing a future in which people can utilize whatever abilities they have to express themselves not only to machines, but to the world.

Many dynamic programming language features, such as implicit declaration, reflection, and code generation, make it difficult to verify the existence of identifiers through standard program analysis. We present an alternative verification, which, rather than analyzing the semantics of code, highlights any name or pair of names that appear only once across a program’s source files. This uniqueness heuristic is implemented for HTML, CSS, and JavaScript, in an interactive editor called Cleanroom, which highlights lone identifiers after each keystroke. Through an online experiment, we show that Cleanroom detects real errors, that it helps developers find these errors more quickly than developers can find them on their own, and that this helps developers avoid costly debugging effort by reducing how many times a program is executed with potential errors. The simplicity and power of Cleanroom’s heuristic may generalize well to other dynamic languages with little support for edit-time name verification.

In recent years, software has become indispensable in complex domains such as science, engineering, biomedicine, and finance. Unfortunately, software developers and user researchers, who are usually experts in programming and Human-Computer Interaction (HCI) methods, respectively, often find that the insight needed to design for complex domains only comes with years of domain experience. How can everyone on a software design team acquire just enough knowledge to design effective software, especially user interfaces, without having to become domain experts? We are performing a series of studies to investigate this question, with the ultimate goal of designing tools to help software teams better capture, manage and explore domain knowledge.

Purpose. To describe the research and development that led to Trackball EdgeWrite, a gestural text entry method that improves desktop input for some people with motor impairments. To compare the character-level version of this technique with a new word-level version. Further, to compare the technique with competitor techniques that use on-screen keyboards. Method. A rapid and iterative design-and-test approach was used to generate working prototypes and elicit quantitative and qualitative feedback from a veteran trackball user. In addition, theoretical modelling based on the Steering law was used to compare competing designs. Results. One result is a refined software artifact, Trackball EdgeWrite, which represents the outcome of this investigation. A theoretical result shows the speed benefit of word-level stroking compared to character-level stroking, which resulted in a 45.0% improvement. Empirical results of a trackball user with a spinal cord injury indicate a peak performance of 8.25 wpm with the character-level version of Trackball EdgeWrite and 12.09 wpm with the word-level version, a 46.5% improvement. Log file analysis of extended real-world text entry shows stroke savings of 43.9% with the word-level version. Both versions of Trackball EdgeWrite were better than on-screen keyboards, particularly regarding user preferences. Follow-up correspondence shows that the veteran trackball user with a spinal cord injury still uses Trackball EdgeWrite on a daily basis 2 years after his initial exposure to the software. Conclusions. Trackball EdgeWrite is a successful new method for desktop text entry and may have further implications for ablebodied users of mobile technologies. Theoretical modelling is useful in combination with empirical testing to explore design alternatives. Single-user lab and field studies can be useful for driving a rapid iterative cycle of innovation and development.

This chapter discusses the measures of text entry performance. These measures, for the most part are, method-agnostic that is applicable to a variety of text entry methods. Speed is one example of a method-agnostic measure. Other measures presented in the chapter are method-specific, inherent to keyboard typing, selector movement, or gestural methods. Typing on a physical keyboard has been studied extensively. Although substitutions, insertions, and omissions characterize a large percentage of typing errors, other errors also appear frequently in typing. These are high-level errors that are unlikely to occur in short transcription typing, but it may occur when participants are composing text or transcribing long documents. Most studies that analyze these types of errors simply report the number or rate of occurrences and give salient examples. Selection-based methods are those that show on-screen options, such as letters in a virtual keyboard, and allow users to select from among them. Selections may be performed directly, such as when tapping with a stylus or clicking with a mouse, or indirectly, when moving a selector with directional arrow keys and entering the highlighted letter by pressing a separate select button.

We are investigating how handheld devices like Palm PDAs and PocketPCs can be used as assistive technologies for computer access by people with motor impairments such as Muscular Dystrophy and Cerebral Palsy. As part of this research, we are developing new input techniques for handheld text entry. People with motor impairments suffer from symptoms that affect their ability to use conventional text entry methods. One symptom is a lack of stability in stylus movements caused by tremor or spasm. In an effort to create a more stable means of text entry, we are researching how to leverage elevated physical edges in our development of new text entry techniques. We present three edge-based techniques: Edge Keyboards, CornerSlide, and EdgeWrite.