Although menu selection has been extensively studied in HCI, most existing studies have focused on sighted users, leaving blind users’ menu selection under-studied. In this paper, we propose a computational model that can simulate blind users’ menu selection performance and strategies, including the way they use techniques like swiping, gliding, and direct touch. We assume that selection behavior emerges as an adaptation to the user’s memory of item positions based on experience and feedback from the screen reader. A key aspect of our model is a model of long-term memory, predicting how a user recalls and forgets item position based on previous menu selections. We compare simulation results predicted by our model against data obtained in an empirical study with ten blind users. The model correctly simulated the effect of the menu length and menu arrangement on selection time, the action composition, and the menu selection strategy of the users.

In this paper, we present AccessWear, a system that improves the accessibility of smartphone touchscreen interactions for blind users using smartwatch gestures. Our system design is human-centered, namely, it incorporates the design goals that were learned from a formative user study with 9 blind participants. The formative study showed that blind users liked the idea of using smartwatch gestures as an alternative: 4 participants liked that when using smart-watch gestures, they did not have to bring their expensive phones out in public and 6 participants liked that smart-watch gestures can be performed with one-hand, as the other hand is usually occupied in holding a cane or a guide dog. Even though there are several advantages to smartwatch gestures, our study also shows that gestures performed by blind users have different patterns compared to sighted users, making gesture recognition more challenging. To this end, AccessWear makes two contributions. The first is a gesture recognition system that works specifically for blind users that is lightweight and does not require per-person training. The second is a near-zero-effort gesture replacement system that does not require any changes to the original application. AccessWear uses input virtualization techniques so that a given gesture can replace the touchscreen input seamlessly. We implement AccessWear on an Android smartphone and Android watch. We perform a quantitative and qualitative study with 8 blind participants. Our study shows that AccessWear can recognize gestures with a 92% accuracy and the end-to-end latency when using an alternate gesture was 53 msec on average. The qualitative study shows that when participants perform a task, consisting of a series of gestures, the system is robust, does not have perceived delays, and does not add physical or mental load on the users.

All graphical user interfaces are comprised of one or more screens that may be shown to the user depending on their interactions. Identifying different screens of an app and understanding the type of changes that happen on the screens is a challenging task that can be applied in many areas including automatic app crawling, playback of app automation macros and large scale app dataset analysis. For example, an automated app crawler needs to understand if the screen it is currently viewing is the same as any previous screen that it has encountered, so it can focus its efforts on portions of the app that it has not yet explored. Moreover, identifying the type of change on the screen, such as whether any dialogues or keyboards have opened or closed, is useful for an automatic crawler to handle such events while crawling. Understanding screen relationships is a difficult task as instances of the same screen may have visual and structural variation, for example due to different content in a database-backed application, scrolling, dialog boxes opening or closing, or content loading delays. At the same time, instances of different screens from the same app may share some similarities in terms of design, structure, and content. This paper uses a dataset of screenshots from more than 1K iPhone applications to train two ML models that understand similarity in different ways: (1) a screen similarity model that combines a UI object detector with a transformer model architecture to recognize instances of the same screen from a collection of screenshots from a single app, and (2) a screen transition model that uses a siamese network architecture to identify both similarity and three types of events that appear in an interaction trace: the keyboard or a dialog box appearing or disappearing, and scrolling. Our models achieve an F1 score of 0.83 on the screen similarity task, improving on comparable baselines, and an average F1 score of 0.71 across all events in the transition task.

We present PaperPal, a wearable smartphone assistant which blind people can use to fill out paper forms independently. Unique features of PaperPal include: a novel 3D-printed attachment that transforms a conventional smartphone into a wearable device with adjustable camera angle; capability to work on both flat stationary tables and portable clipboards; real-time video tracking of pen and paper which is coupled to an interface that generates real-time audio read outs of the form's text content and instructions to guide the user to the form fields; and support for filling out these fields without signature guides. The paper primarily focuses on an essential aspect of PaperPal, namely an accessible design of the wearable elements of PaperPal and the design, implementation and evaluation of a novel user interface for the filling of paper forms by blind people. PaperPal distinguishes itself from a recent work on smartphone-based assistant for blind people for filling paper forms that requires the smartphone and the paper to be placed on a stationary desk, needs the signature guide for form filling, and has no audio read outs of the form's text content. PaperPal, whose design was informed by a separate wizard-of-oz study with blind participants, was evaluated with 8 blind users. Results indicate that they can fill out form fields at the correct locations with an accuracy reaching 96.7%.

Gliding a finger on touchscreen to reach a target, that is, touch exploration, is a common selection method of blind screen-reader users. This paper investigates their gliding behavior and presents a model for their motor performance. We discovered that the gliding trajectories of blind people are a mixture of two strategies: 1) ballistic movements with iterative corrections relying on non-visual feedback, and 2) multiple sub-movements separated by stops, and concatenated until the target is reached. Based on this finding, we propose the mixture pointing model, a model that relates movement time to distance and width of the target. The model outperforms extant models, improving R2 from 0.65 for Fitts’ law to 0.76, and is superior in cross-validation and information criteria. The model advances understanding of gliding-based target selection and serves as a tool for designing interface layouts for screen-reader based touch exploration.

People with low vision who use screen magnifiers to interact with computing devices find it very challenging to interact with dynamically changing digital content such as videos, since they do not have the luxury of time to manually move, i.e., pan the magnifier lens to different regions of interest (ROIs) or zoom into these ROIs before the content changes across frames. In this paper, we present SViM, a first of its kind screen-magnifier interface for such users that leverages advances in computer vision, particularly video saliency models, to identify salient ROIs in videos. SViM's interface allows users to zoom in/out of any point of interest, switch between ROIs via mouse clicks and provides assistive panning with the added flexibility that lets the user explore other regions of the video besides the ROIs identified by SViM. Subjective and objective evaluation of a user study with 13 low vision screen magnifier users revealed that overall the participants had a better user experience with SViM over extant screen magnifiers, indicative of the former's promise and potential for making videos accessible to low vision screen magnifier users.

Navigating webpages with screen readers is a challenge even with recent improvements in screen reader technologies and the increased adoption of web standards for accessibility, namely ARIA. ARIA landmarks, an important aspect of ARIA, lets screen reader users access different sections of the webpage quickly, by enabling them to skip over blocks of irrelevant or redundant content. However, these landmarks are sporadically and inconsistently used by web developers, and in many cases, even absent in numerous web pages. Therefore, we propose SaIL, a scalable approach that automatically detects the important sections of a web page, and then injects ARIA landmarks into the corresponding HTML markup to facilitate quick access to these sections. The central concept underlying SaIL is visual saliency, which is determined using a state-of-the-art deep learning model that was trained on gaze-tracking data collected from sighted users in the context of web browsing. We present the findings of a pilot study that demonstrated the potential of SaIL in reducing both the time and effort spent in navigating webpages with screen readers.

Filling out printed forms (e.g., checks) independently is currently impossible for blind people, since they cannot pinpoint the locations of the form fields, and quite often, they cannot even figure out what fields (e.g., name) are present in the form. Hence, they always depend on sighted people to write on their behalf, and help them affix their signatures. Extant assistive technologies have exclusively focused on reading, with no support for writing. In this paper, we introduce WiYG, a Write-it-Yourself guide that directs a blind user to the different form fields, so that she can independently fill out these fields without seeking assistance from a sighted person. Specifically, WiYG uses a pocket-sized custom 3D printed smartphone attachment, and well-established computer vision algorithms to dynamically generate audio instructions that guide the user to the different form fields. A user study with 13 blind participants showed that with WiYG, users could correctly fill out the form fields at the right locations with an accuracy as high as 89.5%.