Proceedings of the ACM Symposium on User Interface Software and Technology. ACM Symposium on User Interface Software and Technology最新文献

A soft Braille keyboard is a graphical representation of the Braille writing system on smartphones. It provides an essential text input method for visually impaired individuals, but accuracy and efficiency remain significant challenges. We present an intelligent Braille keyboard with auto-correction ability, which uses optimal transportation theory to estimate the distances between touch input and Braille patterns, and combines it with a language model to estimate the probability of entering words. The proposed system was evaluated through both simulations and user studies. In a touch interaction simulation on an Android phone and an iPhone, our intelligent Braille keyboard demonstrated superior error correction performance compared to the Android Braille keyboard with proofreading suggestions and the iPhone Braille keyboard with spelling suggestions. It reduced the error rate from 55.81% on Android and 57.13% on iPhone to 19.80% under high typing noise. Furthermore, in a user study of 12 participants who are legally blind, the intelligent Braille keyboard reduced word error rate (WER) by 59.5% (42.53% to 17.28%) with a slight drop of 0.74 words per minute (WPM), compared to a conventional Braille keyboard without auto-correction. These findings suggest that our approach has the potential to greatly improve the typing experience for Braille users on touchscreen devices.

Cooking is a central activity of daily living, supporting independence as well as mental and physical health. However, prior work has highlighted key barriers for people with low vision (LV) to cook, particularly around safely interacting with tools, such as sharp knives or hot pans. Drawing on recent advancements in computer vision (CV), we present CookAR, a head-mounted AR system with real-time object affordance augmentations to support safe and efficient interactions with kitchen tools. To design and implement CookAR, we collected and annotated the first egocentric dataset of kitchen tool affordances, fine-tuned an affordance segmentation model, and developed an AR system with a stereo camera to generate visual augmentations. To validate CookAR, we conducted a technical evaluation of our fine-tuned model as well as a qualitative lab study with 10 LV participants for suitable augmentation design. Our technical evaluation demonstrates that our model outperforms the baseline on our tool affordance dataset, while our user study indicates a preference for affordance augmentations over the traditional whole object augmentations.

While gesture typing is widely adopted on touchscreen keyboards, its support for low vision users is limited. We have designed and implemented two keyboard prototypes, layout-magnified and key-magnified keyboards, to enable gesture typing for people with low vision. Both keyboards facilitate uninterrupted access to all keys while the screen magnifier is active, allowing people with low vision to input text with one continuous stroke. Furthermore, we have created a kinematics-based decoding algorithm to accommodate the typing behavior of people with low vision. This algorithm can decode the gesture input even if the gesture trace deviates from a pre-defined word template, and the starting position of the gesture is far from the starting letter of the target word. Our user study showed that the key-magnified keyboard achieved 5.28 words per minute, 27.5% faster than a conventional gesture typing keyboard with voice feedback.

Editing operations such as cut, copy, paste, and correcting errors in typed text are often tedious and challenging to perform on smartphones. In this paper, we present VT, a voice and touch-based multi-modal text editing and correction method for smartphones. To edit text with VT, the user glides over a text fragment with a finger and dictates a command, such as "bold" to change the format of the fragment, or the user can tap inside a text area and speak a command such as "highlight this paragraph" to edit the text. For text correcting, the user taps approximately at the area of erroneous text fragment and dictates the new content for substitution or insertion. VT combines touch and voice inputs with language context such as language model and phrase similarity to infer a user's editing intention, which can handle ambiguities and noisy input signals. It is a great advantage over the existing error correction methods (e.g., iOS's Voice Control) which require precise cursor control or text selection. Our evaluation shows that VT significantly improves the efficiency of text editing and text correcting on smartphones over the touch-only method and the iOS's Voice Control method. Our user studies showed that VT reduced the text editing time by 30.80%, and text correcting time by 29.97% over the touch-only method. VT reduced the text editing time by 30.81%, and text correcting time by 47.96% over the iOS's Voice Control method.

Touch point distribution models are important tools for designing touchscreen interfaces. In this paper, we investigate how the finger movement direction affects the touch point distribution, and how to account for it in modeling. We propose the Rotational Dual Gaussian model, a refinement and generalization of the Dual Gaussian model, to account for the finger movement direction in predicting touch point distribution. In this model, the major axis of the prediction ellipse of the touch point distribution is along the finger movement direction, and the minor axis is perpendicular to the finger movement direction. We also propose using projected target width and height, in lieu of nominal target width and height to model touch point distribution. Evaluation on three empirical datasets shows that the new model reflects the observation that the touch point distribution is elongated along the finger movement direction, and outperforms the original Dual Gaussian Model in all prediction tests. Compared with the original Dual Gaussian model, the Rotational Dual Gaussian model reduces the RMSE of touch error rate prediction from 8.49% to 4.95%, and more accurately predicts the touch point distribution in target acquisition. Using the Rotational Dual Gaussian model can also improve the soft keyboard decoding accuracy on smartwatches.

Modeling touch pointing is essential to touchscreen interface development and research, as pointing is one of the most basic and common touch actions users perform on touchscreen devices. Finger-Fitts Law [4] revised the conventional Fitts' law into a 1D (one-dimensional) pointing model for finger touch by explicitly accounting for the fat finger ambiguity (absolute error) problem which was unaccounted for in the original Fitts' law. We generalize Finger-Fitts law to 2D touch pointing by solving two critical problems. First, we extend two of the most successful 2D Fitts law forms to accommodate finger ambiguity. Second, we discovered that using nominal target width and height is a conceptually simple yet effective approach for defining amplitude and directional constraints for 2D touch pointing across different movement directions. The evaluation shows our derived 2D Finger-Fitts law models can be both principled and powerful. Specifically, they outperformed the existing 2D Fitts' laws, as measured by the regression coefficient and model selection information criteria (e.g., Akaike Information Criterion) considering the number of parameters. Finally, 2D Finger-Fitts laws also advance our understanding of touch pointing and thereby serve as the basis for touch interface designs.