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1. A Comparative Study on Single-handed Keyboards on Large-screen Mobile Devices

   https://doi.org/10.1145/3531073.3531075  

   Zhang, Kunpeng; Deng, Zhigang (June 2022, AVI 2022: Proceedings of the 2022 International Conference on Advanced Visual Interfaces)

   Bottoni, Paolo; Panizzi, Emanuele (Ed.)

   Many questions regarding single-hand text entry on modern smartphones (in particular, large-screen smartphones) remain under-explored, such as, (i) will the existing prevailing single-handed keyboards fit for large-screen smartphone users? and (ii) will individual customization improve single-handed keyboard performance? In this paper we study single-handed typing behaviors on several representative keyboards on large-screen mobile devices.We found that, (i) the user-adaptable-shape curved keyboard performs best among all the studied keyboards; (ii) users’ familiarity with the Qwerty layout plays a significant role at the beginning, but after several sessions of training, the user-adaptable curved keyboard can have the best learning curve and performs best; (iii) generally the statistical decoding algorithms via spatial and language models can well handle the input noise from single-handed typing. 

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2. A Dataset of Noisy Typing on QWERTY Keyboards

   https://doi.org/10.1145/3581754.3584174  

   Vertanen, Keith; Kristensson, Per Ola (March 2023, Companion Proceedings of the 28th International Conference on Intelligent User Interfaces)

   Text entry is a common and important part of many intelligent user interfaces. However, inferring a user’s intended text from their input can be challenging: motor actions can be imprecise, input sensors can be noisy, and situations or disabilities can hamper a user’s perception of interface feedback. Numerous prior studies have explored input on touchscreen phones, smartwatches, in midair, and on desktop keyboards. Based on these prior studies, we are releasing a large and diverse data set of noisy typing input consisting of thousands of sentences written by hundreds of users on QWERTY-layout keyboards. This paper describes the various subsets contained in this new research dataset as well as the data format. 

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3. Textflow: Towards Supporting Screen-free Manipulation of Situation-Relevant Smart Messages

   https://doi.org/10.1145/3519263  

   Karimi, Pegah; Plebani, Emanuele; Martin-Hammond, Aqueasha; Bolchini, Davide (February 2022, ACM Transactions on Interactive Intelligent Systems)

   Texting relies on screen-centric prompts designed for sighted users, still posing significant barriers to people who are blind and visually impaired (BVI). Can we re-imagine texting untethered from a visual display? In an interview study, 20 BVI adults shared situations surrounding their texting practices, recurrent topics of conversations, and challenges. Informed by these insights, we introduce TextFlow : a mixed-initiative context-aware system that generates entirely auditory message options relevant to the users’ location, activity, and time of the day. Users can browse and select suggested aural messages using finger-taps supported by an off-the-shelf finger-worn device, without having to hold or attend to a mobile screen. In an evaluative study, 10 BVI participants successfully interacted with TextFlow to browse and send messages in screen-free mode. The experiential response of the users shed light on the importance of bypassing the phone and accessing rapidly controllable messages at their fingertips while preserving privacy and accuracy with respect to speech or screen-based input. We discuss how non-visual access to proactive, contextual messaging can support the blind in a variety of daily scenarios. 

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4. Textflow: Screenless Access to Non-Visual Smart Messaging

   https://doi.org/10.1145/3397481.3450697  

   Karimi, Pegah; Plebani, Emanuele; Bolchini, Davide (April 2021, IUI '21: 26th International Conference on Intelligent User Interfaces)

   null (Ed.)

   Texting relies on screen-centric prompts designed for sighted users, still posing significant barriers to people who are blind and visually impaired (BVI). Can we re-imagine texting untethered from a visual display? In an interview study, 20 BVI adults shared situations surrounding their texting practices, recurrent topics of conversations, and challenges. Informed by these insights, we introduce TextFlow: a mixed-initiative context-aware system that generates entirely auditory message options relevant to the users’ location, activity, and time of the day. Users can browse and select suggested aural messages using finger-taps supported by an off-the-shelf finger-worn device, without having to hold or attend to a mobile screen. In an evaluative study, 10 BVI participants successfully interacted with TextFlow to browse and send messages in screen-free mode. The experiential response of the users shed light on the importance of bypassing the phone and accessing rapidly controllable messages at their fingertips while preserving privacy and accuracy with respect to speech or screen-based input. We discuss how non-visual access to proactive, contextual messaging can support the blind in a variety of daily scenarios. 

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5. Mining, analyzing, and modeling text written on mobile devices

   https://doi.org/10.1017/S1351324919000548  

   Vertanen, K.; Kristensson, P.O. (October 2019, Natural Language Engineering)

   Abstract We present a method for mining the web for text entered on mobile devices. Using searching, crawling, and parsing techniques, we locate text that can be reliably identified as originating from 300 mobile devices. This includes 341,000 sentences written on iPhones alone. Our data enables a richer understanding of how users type “in the wild” on their mobile devices. We compare text and error characteristics of different device types, such as touchscreen phones, phones with physical keyboards, and tablet computers. Using our mined data, we train language models and evaluate these models on mobile test data. A mixture model trained on our mined data, Twitter, blog, and forum data predicts mobile text better than baseline models. Using phone and smartwatch typing data from 135 users, we demonstrate our models improve the recognition accuracy and word predictions of a state-of-the-art touchscreen virtual keyboard decoder. Finally, we make our language models and mined dataset available to other researchers. 

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