Greenspecting Android virtual keyboards

During this still increasing mobile devices proliferation age, much of human-computer interaction involves text input, and the task of typing text is provided via virtual keyboards. In a mobile setting, energy consumption is a key concern for both hardware manufacturers and software developers. Virtual keyboards are software applications, and thus, inefficient applications have a negative impact on the overall energy consumption of the underlying device. Energy consumption analysis and optimization of mobile software is a recent and active area of research. Surprisingly, there is no study analyzing the energy efficiency of the most used software keyboards and evaluating the performance advantage of its features. In this paper, we studied the energy performance of five of the most used virtual keyboards in the Android ecosystem. We measure and analyze the energy consumption in different keyboard scenarios, namely with or without using word prediction. This work presents the results of two studies: one where we instructed the keyboards to simulate the writing of a predefined input text, and another where we performed an empirical study with real users writing the same text. Our studies show that there exist relevant performance differences among the most used keyboards of the considered ecosystem, and it is possible to save nearly 18% of energy by replacing the most used keyboard in Android by the most efficient one. We also showed that is possible to save both energy and time by disabling keyboard intrinsic features and that the use of word suggestions not always compensate for energy and time.- (undefined

Multidimensional Pareto optimization of touchscreen keyboards for speed, familiarity and improved spell checking

The paper presents a new optimization technique for keyboard layouts based on Pareto front optimization. We used this multifactorial technique to create two new touchscreen phone keyboard layouts based on three design metrics: minimizing finger travel distance in order to maximize text entry speed, a new metric to maximize the quality of spell correction quality by minimizing neighbouring key ambiguity, and maximizing familiarity through a similarity function with the standard Qwerty layout. The paper describes the optimization process and resulting layouts for a standard trapezoid shaped keyboard and a more rectangular layout. Fitts' law modelling shows a predicted 11% improvement in entry speed without taking into account the significantly improved error correction potential and the subsequent effect on speed. In initial user tests typing speed dropped from approx. 21wpm with Qwerty to 13wpm (64%) on first use of our layout but recovered to 18wpm (85%) within four short trial sessions, and was still improving. NASA TLX forms showed no significant difference on load between Qwerty and our new layout use in the fourth session. Together we believe this shows the new layouts are faster and can be quickly adopted by users

Analyzing the Impact of Cognitive Load in Evaluating Gaze-based Typing

Gaze-based virtual keyboards provide an effective interface for text entry by eye movements. The efficiency and usability of these keyboards have traditionally been evaluated with conventional text entry performance measures such as words per minute, keystrokes per character, backspace usage, etc. However, in comparison to the traditional text entry approaches, gaze-based typing involves natural eye movements that are highly correlated with human brain cognition. Employing eye gaze as an input could lead to excessive mental demand, and in this work we argue the need to include cognitive load as an eye typing evaluation measure. We evaluate three variations of gaze-based virtual keyboards, which implement variable designs in terms of word suggestion positioning. The conventional text entry metrics indicate no significant difference in the performance of the different keyboard designs. However, STFT (Short-time Fourier Transform) based analysis of EEG signals indicate variances in the mental workload of participants while interacting with these designs. Moreover, the EEG analysis provides insights into the user's cognition variation for different typing phases and intervals, which should be considered in order to improve eye typing usability.Comment: 6 pages, 4 figures, IEEE CBMS 201

OpenAdaptxt: an open source enabling technology for high quality text entry

Modern text entry systems, especially for touch screen phones and novel devices, rely on complex underlying technologies such as error correction and word suggestion. Furthermore, for global deployment a vast number of languages have to be supported. Together this has raised the entry bar for new text entry techniques, which makes developing and testing a longer process thus stifling innovation. For example, testing a new feedback mechanism in comparison to a stock keyboard now requires the researchers to support at least slip correction and probably word suggestion. This paper introduces OpenAdaptxt: an open source community driven text input platform to enable development of higher quality text input solutions. It is the first commercial-grade open source enabling technology for modern text entry that supports both multiple platforms and dictionary support for over 50 spoken languages

Ability-Based Methods for Personalized Keyboard Generation

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.Comment: 20 pages, 7 figure