1 thumb, 4 buttons, 20 words per minute

MacKenzie, I. Scott; Soukoreff, R. William; Helga, Joanna

doi:10.1145/2047196.2047258

Cited by 42 publications

(3 citation statements)

References 22 publications

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“…Thus, switch users rely on consistent and accessible app designs to be able to anticipate the future UI elements that will be focused by the scanning service. One example that highlights how tedious this input can be is related to typing speed, where switch users average 3 words per minute (wpm), and touch-based users average closer to 40 wpm [55].…”

Section: Background and Motivationmentioning

confidence: 99%

“…This allows a motor-impaired user to easily select the icon or component with which they wish to interact. However, this process can be slower than traditional gesture-based control [55], and switch users often rely on the consistency of certain UI element patterns, such as menus, in order to quickly perform actions. As such, a popular motor-impairment accessibility design guideline for mobile apps [1,13] states that persistent icons that appear across multiple screens, such as tab bars or menus, should retain a consistent ordering.…”

Section: Introductionmentioning

confidence: 99%

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MotorEase: Automated Detection of Motor Impairment Accessibility Issues in Mobile App UIs

Krishna Vajjala,

Mansur,

Jose

et al. 2024

Proceedings of the IEEE/ACM 46th International Conference on Software Engineering

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Recent research has begun to examine the potential of automatically finding and fixing accessibility issues that manifest in software. However, while recent work makes important progress, it has generally been skewed toward identifying issues that affect users with certain disabilities, such as those with visual or hearing impairments. However there are other groups of users with different types of disabilities that also need software tooling support to improve their experience. As such, this paper aims to automatically identify accessibility issues that affect users with motor-impairments.To move toward this goal, this paper introduces a novel approach, called MotorEase, capable of identifying accessibility issues in mobile app UIs that impact motor-impaired users. Motor-impaired users often have limited ability to interact with touch-based devices, and instead may make use of a switch or other assistive mechanismhence UIs must be designed to support both limited touch gestures and the use of assistive devices. MotorEase adapts computer vision and text processing techniques to enable a semantic understanding of app UI screens, enabling the detection of violations related to four popular, previously unexplored UI design guidelines that support motor-impaired users, including: (i) visual touch target size, (ii) expanding sections, (iii) persisting elements, and (iv) adjacent icon visual distance. We evaluate MotorEase on a newly derived benchmark, called MotorCheck, that contains 555 manually annotated examples of violations to the above accessibility guidelines, across 1599 screens collected from 70 applications via a mobile app testing tool. Our experiments illustrate that MotorEase is able to identify violations with an average accuracy of ≈90%, and a false positive rate of less than 9%, outperforming baseline techniques. CCS CONCEPTS• Software and its engineering → Software testing and debugging; Software design engineering.

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Section: Background and Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MotorEase: Automated Detection of Motor Impairment Accessibility Issues in Mobile App UIs

Krishna Vajjala,

Mansur,

Jose

et al. 2024

Proceedings of the IEEE/ACM 46th International Conference on Software Engineering

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“…A continuous approach, such as EdgeWrite, maps a word to a continuous stroke [18]. A discrete approach, such as H4-Writer, uses a base-4 Huffman coding to generate unique key sequences for each letter [8].…”

Section: Related Workmentioning

confidence: 99%

Swipeboard

Chen

Grossman

Fitzmaurice

2014

Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology

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Ultra-small smart devices, such as smart watches, have become increasingly popular in recent years. Most of these devices rely on touch as the primary input modality, which makes tasks such as text entry increasingly difficult as the devices continue to shrink. In the sole pursuit of entry speed, the ultimate solution is a shorthand technique (e.g., Morse code) that sequences tokens of input (e.g., key, tap, swipe) into unique representations of each character. However, learning such techniques is hard, as it often resorts to rote memory. Our technique, Swipeboard, leverages our spatial memory of a QWERTY keyboard to learn, and eventually master a shorthand, eyes-free text entry method designed for ultra-small interfaces. Characters are entered with two swipes; the first swipe specifies the region where the character is located, and the second swipe specifies the character within that region. Our study showed that with less than two hours' training, Tested on a reduced word set, Swipeboard users achieved 19.58 words per minute (WPM), 15% faster than an existing baseline technique.

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TEXTile: Eyes-Free Text Input on Smart Glasses Using Touch Enabled Textile on the Forearm

Belkacem

Pecci

Martin

et al. 2019

Human-Computer Interaction – INTERACT 2019

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Smart glasses are autonomous and efficient computers that can perform complex tasks through mobile applications. This paper focuses on text input for mobile context. We present a new connected fabric to smart glasses as a device for text entry. This device, integrated into clothing, provides a new interaction technique called TEXTile. It allows typing without requiring users to hold a device. Users can put fingers anywhere on the fabric surface and release them, without needing to look at the fabric or using markers on the fabric. The text entry technique is based on eight combinations of fingers in contact or released, identified as pleasant among 15 in a survey involving 74 participants. A first user's study with 20 participants establishes that the eight combinations for TEXTile were significantly reliable (98.95% recognition rate). A second study with nine participants evaluates the learning curve of TEXTile. Users achieved a mean typing of 8.11 WPM at the end of ten 12-minute sessions, which can be slow, but sufficient with short text compared to other advantages of the technique. Results show low error rates for tasks completed and good usability (76% in SUS questionnaire). The NASA-TLX questionnaire establishes there is no important mental or physical workload to accomplish the task.

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1 thumb, 4 buttons, 20 words per minute

Cited by 42 publications

References 22 publications

MotorEase: Automated Detection of Motor Impairment Accessibility Issues in Mobile App UIs

MotorEase: Automated Detection of Motor Impairment Accessibility Issues in Mobile App UIs

Swipeboard

TEXTile: Eyes-Free Text Input on Smart Glasses Using Touch Enabled Textile on the Forearm

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