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arXiv · 2201.04593

Ability-Based Methods for Personalized Keyboard Generation

Claire L. Mitchell·Gabriel J. Cler·Susan K. Fager·Paola Contessa·Serge H. Roy·Gianluca De Luca·Joshua C. Kline·Jennifer M. Vojtech
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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.

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Claire L. Mitchell, Gabriel J. Cler, Susan K. Fager, Paola Contessa, Serge H. Roy, Gianluca De Luca, Joshua C. Kline, Jennifer M. Vojtech. 2022-01-12. Ability-Based Methods for Personalized Keyboard Generation. https://doi.org/10.3390/mti6080067

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