Ability-Based Methods for Personalized Keyboard Generation

Abstract

This study introduces an ability-based method for personalized keyboard generation, wherein an individual’s own movement and human–computer interaction data are used to automatically compute a personalized virtual keyboard layout. Our approach integrates a multidirectional point-select task to characterize cursor control over time, distance, and direction. The characterization is automatically employed to develop a computationally efficient keyboard layout that prioritizes each user’s movement abilities through capturing directional constraints and preferences. We evaluated our approach in a study involving 16 participants using inertial sensing and facial electromyography as an access method, resulting in significantly increased communication rates using the personalized keyboard (52.0 bits/min) when compared to a generically optimized keyboard (47.9 bits/min). Our results demonstrate the ability to effectively characterize an individual’s movement abilities to design a personalized keyboard for improved communication. This work underscores the importance of integrating a user’s motor abilities when designing virtual interfaces.

Ability-based Keyboards for Augmentative and Alternative Communication: Understanding How Individuals’ Movement Patterns Translate to More Efficient Keyboards

“…This work expands on novel methods for ability-based keyboard generation in individuals without dexterity impairments [ 23 ], as well as prior work on generically optimizing a keyboard to improve communication in individuals with dexterity impairments due to motor disabilities [ 4 ]. Here, we examine the feasibility of ability-based keyboard generation for those with motor disabilities.…”

“…Here, we examine the feasibility of ability-based keyboard generation for those with motor disabilities. We briefly summarize (see [ 23 ]) steps to algorithmically characterize movement and generate personalized keyboards below.…”

“…Targets are seeded to capture two-dimensional (2D) movements across a range of possible distances and directions. Each target is categorized into one of 16 pre-specified bins spanning (360/16 =) 22.5 degrees by the empirical angle of target selection relative to the previous target [ 23 ]. Within each bin, a linear regression is performed to yield constants a and b from Mackenzie’s Shannon formulation of Fitts’ Law ( Equation 1 ) [ 16 , 17 ]: Specifically, the movement time (MT; sec) to select a given target is calculated as the travel time of the cursor between those clicks; distance (D; pixels) is calculated as the Euclidean distance between those click locations.…”

“…Text input performance was evaluated for each participant when using a keyboard generated via our personalization methods versus that when using QWERTY or a generic, computationally optimized keyboard. To enable comparisons with QWERTY, the width of the space key on the QWERTY keyboard was set equal to all other keys and positioned to the right of the “M” key (see [ 23 ]), as prior work has shown that text input performance does not significantly differ in this configuration [ 23 ]. Distinct from the personalized keyboards, which are derived from user-specific a and b constants that vary according to movement direction, the generically optimized keyboard was generated using standard directionally static Fitts’ constants of a = 0.127 sec and b = 1/4.9 sec/bits across all target selection angles [ 21 , 30 ].…”