Perceived accessibility of an application is a subjective measure of how well an individual with a particular disability, skills, and goals experiences the application via assistive technology. This paper first presents a study with 11 blind users to report how they perceive the accessibility of desktop applications while interacting via assistive technology such as screen readers and a keyboard. The study identifies the low navigational complexity of the user interface (UI) elements as the primary contributor to higher perceived accessibility of different applications. Informed by this study, we develop a probabilistic model that accounts for the number of user actions needed to navigate between any two arbitrary UI elements within an application. This model contributes to the area of computational interaction for non-visual interaction. Next, we derive three metrics from this model: complexity, coverage, and reachability, which reveal important statistical characteristics of an application indicative of its perceived accessibility. The proposed metrics are appropriate for comparing similar applications and can be fine-tuned for individual users to cater to their skills and goals. Finally, we present five use cases, demonstrating how blind users, application developers, and accessibility practitioners can benefit from our model and metrics.
Older adults increasingly adopt small-screen devices, but limited motor dexterity hinders their ability to type effectively. While a 9-key (T9) keyboard allocates larger space to each key, it is shared by multiple consecutive letters. Consequently, users must interrupt their gestures when typing consecutive letters, leading to inefficiencies and poor user experience. Thus, we proposed a novel keyboard that leverages the currently unused key 1 to duplicate letters from the previous key, allowing the entry of consecutive letters without interruptions. A user study with 12 older adults showed that it significantly outperformed the T9 with wiggle gesture in typing speed, KSPC, insertion errors, and deletes per word while achieving comparable performance as the conventional T9. Repeating the typing tasks with 12 young adults found that the advantages of the novel T9 were consistent or enhanced. We also provide error analysis and design considerations for improving gesture typing on T9 for older adults.
Single-sided deafness (SSD) significantly restricts social participation in hearing/speaking cultures due to the person's difficulty hearing conversations on their deaf side. Although hearing aids for SSD are effective in social situations, the acceptance rate remains low at 4%. To address this problem, we designed and developed a bone conduction-based device to be worn with eyeglasses, involving 53 individuals with SSD including two authors. We conducted a four-week diary study comparing our proposed device with traditional Contralateral Routing of Signals (CROS) hearing aids and explored the factors that might affect the acceptance rate of assistive devices for SSD. The findings indicated that our design was more acceptable for users with SSD due to its effectiveness, social acceptability, and the ability for wearers to use other devices simultaneously, such as earbuds. Based on our results, we discuss implications for designing wearable assistive devices to promote greater acceptance among the target population.
Mobile text entry is difficult for people with motor impairments due to limited access to smartphones and the need for precise target selection on touchscreens. Text entry on smartwatches, on the other hand, has not been well explored for the population. Crownboard enables people with limited dexterity enter text on a smartwatch using its crown. It uses an alphabetical layout divided into eight zones around the bezel. The zones are scanned either automatically or manually by rotating the crown, then selected by pressing the crown. Crownboard decodes zone sequences into words and displays word suggestions. We validated its design in multiple studies. First, a comparison between manual and automated scanning revealed that manual scanning is faster and more accurate. Second, a comparison between clockwise and shortest-path scanning identified the former to be faster and more accurate. In the final study with representative users, only 30% participants could use the default Qwerty. They were 9% and 23% faster with manual and automated Crownboard, respectively. All participants were able to use both variants of Crownboard.
Physical reading rulers are simple yet effective interventions that help readers with dyslexia. Digital reading rulers may offer similar benefits. Given their potential value, we provide the following contributions: (1) We host focus group sessions including people with dyslexia to build upon their lived experiences, (2) We provide evidence for designs that are effective and preferred, (3) We measure reading gains of rulers for readers with and without dyslexia. Using inclusive design principles, we arrive at four digital ruler designs - Grey Bar, Lightbox, Shade, and Underline. For the first time, we offer a comprehensive evaluation of digital ruler effectiveness on 91 crowdsourced readers with dyslexia and 86 without. Considering reading speed, comprehension, and preference, many readers benefit from these rulers, with the largest gains among readers with dyslexia. Rulers designed by readers with dyslexia increased the reading speeds of readers with dyslexia, supporting the need for inclusive design practices.
The Web has become an essential part of many people’s daily lives, enabling them to complete everyday and essential tasks online and access important information resources. The ability to navigate the Web via the keyboard interface is critical to people with various types of disabilities. However, modern websites often violate web accessibility guidelines for keyboard navigability. In this paper, we present a novel approach for automatically detecting web accessibility barriers that prevent or hinder keyboard users' ability to navigate web pages. An extensive evaluation of our technique on real-world subjects showed that our technique was able to detect navigation-based keyboard accessibility barriers in web applications with high precision and recall.