Modelling and correcting for the impact of the gait cycle on touch screen typing accuracy

Walking and typing on a smartphone is an extremely common interaction. Previous research has shown that error rates are higher when walking than when stationary. In this paper we analyse the acceleration data logged in an experiment in which users typed whilst walking, and extract the gait phase angle. We find statistically significant relationships between tapping time, error rate and gait phase angle. We then use the gait phase as an additional input to an offset model, and show that this allows more accurate touch interaction for walking users than a model which considers only the recorded tap position

Are Split Tablet Keyboards Better? A Study of Soft Keyboard Layout and Hand Posture

acceptedVersio

Comprensión de las posturas de dedos al tocar objetivos en la pantalla táctil de dispositivos móviles

This paper presents the results of a preliminary study on the interaction of the fingertip and a mobile device’s touch screen. The objective of this study is to identify the finger postures that will be included in the main study on the contact area in the interaction between fingertip and a flat surface. Twenty participants (15 males and 5 females) took part in this study. They were asked to complete two tasks in a sitting posture. In the first task, they had to touch targets on the mobile device screens by tapping them sequentially, while in the second task they were asked to connect the targets with straight lines. The results showed that the participants used mainly their thumbs and index fingers to touch targets on the screen of the devices. Only a small number of participants used their middle finger, and only in a few touching activities.Este trabajo presenta los resultados de un estudio preliminar sobre la interacción de la yema del dedo y la pantalla táctil de un dispositivo móvil. El objetivo del estudio es identificar posturas de los dedos que se incluirán en el estudio principal en el área de contacto en la interacción entre las puntas de los dedos y una superficie plana. Veinte participantes (15 hombres), participaron en este estudio. Se les pidió que completasen dos tareas en una postura sentada. En la primera tarea, tuvieron que tocar objetivos en las pantallas de dispositivos móviles de una forma secuencial, mientras que en la segunda tarea se les pidió que conectasen posiciones objetivo con líneas rectas. Los resultados mostraron que los participantes utilizan principalmente sus dedos pulgares e indicadores para tocar posiciones objetivo en la pantalla de los dispositivos. Sólo un pequeño número de participantes usó su dedo medio, y en pocas actividades.(undefined

Does emotion influence the use of auto-suggest during smartphone typing?

Typing based interfaces are common across many mobile applications, especially messaging apps. To reduce the difficulty of typing using keyboard applications on smartphones, smartwatches with restricted space, several techniques, such as auto-complete, auto-suggest, are implemented. Although helpful, these techniques do add more cognitive load on the user. Hence beyond the importance to improve the word recommendations, it is useful to understand the pattern of use of auto-suggestions during typing. Among several factors that may influence use of auto-suggest, the role of emotion has been mostly overlooked, often due to the difficulty of unobtrusively inferring emotion. With advances in affective computing, and ability to infer user's emotional states accurately, it is imperative to investigate how auto-suggest can be guided by emotion aware decisions. In this work, we investigate correlations between user emotion and usage of auto-suggest i.e. whether users prefer to use auto-suggest in specific emotion states. We developed an Android keyboard application, which records auto-suggest usage and collects emotion self-reports from users in a 3-week in-the-wild study. Analysis of the dataset reveals relationship between user reported emotion state and use of auto-suggest. We used the data to train personalized models for predicting use of auto-suggest in specific emotion state. The model can predict use of auto-suggest with an average accuracy (AUCROC) of 82% showing the feasibility of emotion-aware auto-suggestion

Comparing Evaluation Methods for Encumbrance and Walking on Interaction with Touchscreen Mobile Devices

In this paper, two walking evaluation methods were compared to evaluate the effects of encumbrance while the preferred walking speed (PWS) is controlled. Users frequently carry cumbersome objects (e.g. shopping bags) and use mobile devices at the same time which can cause interaction difficulties and erroneous input. The two methods used to control the PWS were: walking on a treadmill and walking around a predefined route on the ground while following a pacesetter. The results from our target acquisition experiment showed that for ground walking at 100% of PWS, accuracy dropped to 36% when carrying a bag in the dominant hand while accuracy reduced to 34% for holding a box under the dominant arm. We also discuss the advantages and limitations of each evaluation method when examining encumbrance and suggest treadmill walking is not the most suitable approach to use if walking speed is an important factor in future mobile studies

HTML5 Based Email Client with Touch Enabled Advanced User Interface for Tabs and Tablets

The Internet has become as a very powerful platform that has changed the way we do business and the way we communicate. E-Mail is an essential tool for both cooperative world and individuals for communicating. Web-based e-mail systems have become more popular among the internet users with time. Internet and the devices that we use to access the internet are rapidly changing time to time. Among the recent evolutions the most significant of them is HTML5 incorporated mobile technology with hi-tech devices like tabs, iPads and tablets with touch sensitivity. However, the major problem occurs when different levels of resolutions arisewithmodern devices. Some devices support touch, multi-touch, gestures, keyboards and stylus. User-interface of email web clients hasnt improved in the way hi-tech devices evolved. Handling different types of interactions depends on the device and way that user handles it. We have developed HTML5 off-line supporting web-based UI for e-mail system to overcome this issue and to provide a highly user based interactive, responsive and efficient process even in slow network connections. Our approach is based on HTML5 features and client side on java-scripting. Our system is capable of running on a browser without installing any plug-ins. Depending on the device resolution and user interaction (one finger touch/ both hands or external keyboard) email client has provisions to transform the web UI to give better interaction for the user and email system

A glimpse of mobile text entry errors and corrective behaviour in the wild

Research in mobile text entry has long focused on speed and input errors during lab studies. However, little is known about how input errors emerge in real-world situations or how users deal with these. We present findings from an in-the-wild study of everyday text entry and discuss their implications for future studies

Spatial model personalization in Gboard

We introduce a framework for adapting a virtual keyboard to individual user behavior by modifying a Gaussian spatial model to use personalized key center offset means and, optionally, learned covariances. Through numerous real-world studies, we determine the importance of training data quantity and weights, as well as the number of clusters into which to group keys to avoid overfitting. While past research has shown potential of this technique using artificially-simple virtual keyboards and games or fixed typing prompts, we demonstrate effectiveness using the highly-tuned Gboard app with a representative set of users and their real typing behaviors. Across a variety of top languages, we achieve small-but-significant improvements in both typing speed and decoder accuracy.Comment: 17 pages, to be published in the Proceedings of the 24th International Conference on Mobile Human-Computer Interaction (MobileHCI 2022