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Selection-based virtual keyboard prototypes and data collection application
Abstract and Figures
An emerging area of research in engineering psychology is the evaluation of text entry for mobile devices using a small number of keys for the control of cursor direction and character selection from a matrix of characters (i.e., selection-based data entry). The present article describes a software tool designed to reduce time and effort in the development of prototypes of alternative selection-based text-entry schemes and their empirical evaluation. The tool, available for distribution to researchers, educators, and students, uses Action Script code compiled into an executable file that has an embedded Adobe Flash Player and is compatible with most operating systems (including Microsoft Windows, Apple OSX, and Linux).
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... Yes , (Arif and Stuerzlinger, 2013), (Corley et al., 2012), (Francis and Johnson, 2011), (Gelormini and Bishop, 2013), (Ghosh, 2011), (Guerrier et al., 2013), (Janpinijrut et al., 2011b), (Kim et al., 2014), (Klima and Slovacek, 2009), (Merino et al., 2010), (Millet et al., 2009), (Nicolau et al., 2013), (Prabhu and Prasad, 2011), (Sharma et al., 2012), (Simathamanand and Piromsopa, 2011), (Usakli and Gurkan, 2010), (Vella and Vigouroux, 2013), (Wu et al., 2014) No , (Arif and Stuerzlinger, 2013), (Arif and Stuerzlinger, 2013), (Bhattacharya and Laha, 2012), (Burgbacher and Hinrichs, 2014), (Corley et al., 2012), (Eklund et al., 2010), (Faraj et al., 2009c), (Faraj et al., 2009a), (Faraj et al., 2009b), (Francis and Johnson, 2011), (Gelormini and Bishop, 2013), (Ghosh, 2011), (Gizatdinova et al., 2012a), (Gizatdinova et al., 2012b), (Guerrier et al., 2013), (Jain and Bhattacharya, 2010), (Janpinijrut et al., 2011a), (Kim et al., 2014), (Klima and Slovacek, 2009), (Merino et al., 2010), (Millet et al., 2009), (Miró-Borrás and Bernabeu-Soler, 2009), (Molina et al., 2009b), (Nicolau et al., 2013), (Panwar et al., 2012), (Prabhu and Prasad, 2011), (Sarafis and Markoulidis, 2010), (Sharma et al., 2012), (Simathamanand and Piromsopa, 2011), (Usakli and Gurkan, 2010), (Vella and Vigouroux, 2013), (Vella and Vigouroux, 2013), (Wu et al., 2014) Key size (Faraj et al., 2009c), (Faraj et al., 2009a), (Gelormini and Bishop, 2013), , (Nicolau et al., 2013), (Varcholik et al., 2012), (Wu et al., 2014),, ...
... Yes , (Arif and Stuerzlinger, 2013), (Corley et al., 2012), (Francis and Johnson, 2011), (Gelormini and Bishop, 2013), (Ghosh, 2011), (Guerrier et al., 2013), (Janpinijrut et al., 2011b), (Kim et al., 2014), (Klima and Slovacek, 2009), (Merino et al., 2010), (Millet et al., 2009), (Nicolau et al., 2013), (Prabhu and Prasad, 2011), (Sharma et al., 2012), (Simathamanand and Piromsopa, 2011), (Usakli and Gurkan, 2010), (Vella and Vigouroux, 2013), (Wu et al., 2014) No , (Arif and Stuerzlinger, 2013), (Arif and Stuerzlinger, 2013), (Bhattacharya and Laha, 2012), (Burgbacher and Hinrichs, 2014), (Corley et al., 2012), (Eklund et al., 2010), (Faraj et al., 2009c), (Faraj et al., 2009a), (Faraj et al., 2009b), (Francis and Johnson, 2011), (Gelormini and Bishop, 2013), (Ghosh, 2011), (Gizatdinova et al., 2012a), (Gizatdinova et al., 2012b), (Guerrier et al., 2013), (Jain and Bhattacharya, 2010), (Janpinijrut et al., 2011a), (Kim et al., 2014), (Klima and Slovacek, 2009), (Merino et al., 2010), (Millet et al., 2009), (Miró-Borrás and Bernabeu-Soler, 2009), (Molina et al., 2009b), (Nicolau et al., 2013), (Panwar et al., 2012), (Prabhu and Prasad, 2011), (Sarafis and Markoulidis, 2010), (Sharma et al., 2012), (Simathamanand and Piromsopa, 2011), (Usakli and Gurkan, 2010), (Vella and Vigouroux, 2013), (Vella and Vigouroux, 2013), (Wu et al., 2014) Key size (Faraj et al., 2009c), (Faraj et al., 2009a), (Gelormini and Bishop, 2013), , (Nicolau et al., 2013), (Varcholik et al., 2012), (Wu et al., 2014),, ...
... Frequency of Occurrence (Merino et al., 2012), N-gram (Sharma et al., 2012), (Ghosh, 2011) Regency of Use (Bhattacharya and Laha, 2012), ) Words table probability (Guerrier et al., 2011), (Merino et al., 2010), (Truong et al., 2013) Syntactic Probability Table Prediction not specified (Bhattacharya and Laha, 2012), (Guerrier et al., 2011), (Guerrier et al., 2013), (Guerrier et al., 2013), (Janpinijrut et al., 2011b), (Molina et al., 2009b), (Prabhu and Prasad, 2011), (Topal et al., 2012), (Topal et al., 2012) Organization of keys and symbols Evolutionary Algorithm (Guerrier et al., 2011), (Francis and Johnson, 2011) N-gram , (Ghosh, 2011) Digraph Frequency (Gelormini and Bishop, 2013), (Millet et al., 2009), Single character Frequency (Gelormini and Bishop, 2013), , (Guerrier et al., 2013), (Jain and Bhattacharya, 2010), (Merino et al., 2012), (Millet et al., 2009), (Panwar et al., 2012), (Prabhu and Prasad, 2011), (Topal et al., 2012) Fitts' Law (Faraj et al., 2009a, b), , (Ghosh, 2011) Fitts' Digraph Model , (Ghosh, 2011), (Jain and Bhattacharya, 2010) Disambiguation methods Disambiguation algorithm (Merino et al., 2010), (Miró-Borrás and Bernabeu-Soler, 2009), (Molina et al., 2009b), (Sarafis and Markoulidis, 2010) Multi-tap (Burgbacher and Hinrichs, 2014), (Guerrier et al., 2011), (Molina et al., 2009b), (Prabhu and Prasad, 2011), (Sarafis and Markoulidis, 2010) Keyboard user adaptation (Bhattacharya and Laha, 2012), , (Merino et al., 2010) that adopt the alphabetical distribution present letters "a" to "z" sequentially distributed over the keyboard. Other example is QWERTY distribution. ...
Introduction: Due to the increasing popularization of computers and the internet expansion, Alternative and Augmentative Communication technologies have been employed to restore the ability to communicate of people with aphasia and tetraplegia. Virtual keyboards are one of the most primitive mechanisms for alternatively entering text and play a very important role in accomplishing this task. However, the text entry for this kind of keyboard is much slower than entering information through their physical counterparts. Many techniques and layouts have been proposed to improve the typing performance of virtual keyboards, each one concerning a different issue or solving a specific problem. However, not all of them are suitable to assist seriously people with motor impairment. Methods: In order to develop an assistive virtual keyboard with improved typing performance, we performed a systematic review on scientific databases. Results: We found 250 related papers and 52 of them were selected to compose. After that, we identified eight essentials virtual keyboard features, five methods to optimize data entry performance and five metrics to assess typing performance. Conclusion: Based on this review, we introduce a concept of an assistive, optimized, compact and adaptive virtual keyboard that gathers a set of suitable techniques such as: a new ambiguous keyboard layout, disambiguation algorithms, dynamic scan techniques, static text prediction of letters and words and, finally, the use of phonetic and similarity algorithms to reduce the user's typing error rate.
... The research design reveals the path to answering the research problems through specific research strategies (Millet, 2009). Three main research approaches including quantitative, qualitative, and mixed are used by researchers. ...
The waiting time is a major challenge for government hospitals around the world, particularly in developing countries, due to inefficient design and limited facilities. This waiting time causes long queues, especially in service providing facilities. Hence, queuing is considered a key performance indicator when evaluating the performance of healthcare facilities. The outpatient department (OPD) is the main division of a hospital that handles a large number of patients daily. This research aimed to provide solutions for minimising waiting time in OPD premises through efficient planning and designing strategies. This study applied the queuing theory for two case studies to analyse the waiting time of the patients at the OPD. Hospital records and field observations were used to gather data. Further solutions for minimising the waiting time were identified using semi-structured interviews with hospital management and a questionnaire survey with patients at OPD. Field observations revealed that there were long waiting queues and long waiting times at the registration counter and the consultant rooms. Due to increased demand for OPD services, unnecessary arrivals, a lack of resources, and patients’ ignorance of OPD procedures were identified as causes of overcrowding. Further, to minimise waiting time at the OPD, it should leverage the efficient designs with properly placed inquiry counters, walkways, and directions; provide adequate facilities such as a spacious waiting area, and restructuring should be implemented. The findings of the research mark valuable insights into government hospitals, and the proposed solutions will be useful for hospital management.
... I have twice had the opportunity to participate on the doctoral committees of interns with whom I have worked, one from the University of Central Florida and the other from the University of Miami. Both interns conducted research that had strong industrial value while also accomplishing the research goals required for their doctorates [19,20]. ...
This paper presents perspectives from both academia and practice on how both groups can collaborate and work together to create synergy in the development and advancement of human-computer interaction (HCI). Issues and challenges are highlighted, success cases are offered as examples, and suggestions are provided to further such collaborations.
... Guidelines for user interface are widely available (e.g., Apple UI Guidelines, see [18]). Important criteria like accuracy, speed, error correction, and mechanical and environmental factors (battery life, screen visibility, resistance to adverse weather conditions) are identified ( [19]; [3]; [17]). ...
Direct behavior observation, i.e., without first creating a video recording, is a challenging, one-shot task. The behavior has to be coded accurately during the situation itself. Mobile devices can assist direct observation, and there already are applications available for these purposes. However, the mobile revolution has led to new developments in devices, infrastructure, and market penetration that have opened up new, yet untapped, possibilities. In this article, expanded activity theory is used to highlight the unused potential of computer assisted direct observation (CADO) apps. If this potential is realized, it can provide observation with the same advantages online questionnaires and sites like Mechanical Turk have provided for surveys and Internet experiments.
... This tool (Millet, Asfour, & Lewis, 2009) supports the development and evaluation of selection-based virtual keyboards. It takes as input xml files that define test phrases and keyboard layouts. ...
Despite the increase in popularity of handheld devices, text entry on such devices is becoming more difficult due to reduced form factors that limit display size, input modes, and interaction techniques. In an effort to circumvent these issues, research has found that five-key methods are effective for text entry on devices such as in-car navigation systems, television and gaming controllers, wrist watches, and other small devices. Five-key text entry methods use four directional keys to move a selector over an on-screen keyboard and an Enter key for selection. Although other researchers have described five-key character layouts using alphabetical order and predictive layouts based on digraph frequencies, there is considerable latitude in designing the rest of a comprehensive on-screen keyboard. Furthermore, it might be possible to capitalize on the relative strengths of the alphabetic and predictive layouts by combining them in a hybrid layout. Thus, this research examines the design of alternative keyboard layouts for five-key text entry techniques. Three keyboard layouts (Alphabetical, Predictive, and Hybrid) were selected to represent standard and less familiar arrangements. The analysis centered on a series of controlled experiments conducted on a research platform designed by the author. In this work, when the immediate usability of three alternative keyboard layouts for supporting five-key text entry was investigated, results indicated no statistically significant differences in performance across the tested keyboards. Furthermore, experimental results show that following immediate usability, but still at the onset of learning, there was no overall difference in performance among the three keyboard layouts across four text types. However, the Alphabetical keyboard surpassed both the Predictive and Hybrid keyboards in text entry speed in typing Web addresses. The nonstandard keyboards performed superior to the Alphabetical keyboards in typing Words/Spaces and Sentences, but performed no better in typing Address strings than the Alphabetical. Use of mixed effects modeling suggested that the longitudinal data was best fitted by a quadratic model. Text entry performance on all three layouts improved as a function of practice, demonstrating that participants could learn the unfamiliar layouts to complete text entry tasks. Overall, there was no indication that use of nonstandard layouts impedes performance. In fact, trend in time data suggests that the learning rates were greater for the nonstandard keyboards over the standard layout. Overall, participants preferred the Hybrid layout. In summary, this dissertation focused on creating and validating novel and effective five-key text entry techniques for constrained devices.
Remote controls are especially problematic for writing text, something necessary in many interactive digital TV applications (IDTV). In the context of research aimed at finding effective entry methods for IDTV applications, an empirical investigation was carried out and is presented in this article. The study analyzed the errors committed by 82 users in an experiment in which they wrote 7,395 sentences. Several typing methods suitable for IDTV contexts were used for experimentation. There were 3,562 of the sentences that registered at least one error. These errors were classified and their main causes analyzed, the particular characteristics of the users taken into account. The results show that the most frequent errors are proximity mistakes in all the evaluated methods. They appear in between 13.75% and 32.31% of the sentences, depending on the input method. Also, age and background are major aspects affecting the number and type of mistakes. The results could be the basis of changes in the design of conventional remote controls or in the design of advanced techniques for error recognition and correction.
Keyboards are in widespread use both on typewriters and as input devices to computers. Early refinements of the typewriter keyboard aimed at improving its mechanical action so that it would operate more smoothly with fewer malfunctions. Later, the work focused on improving typing speed and accuracy. This chapter describes keyboard design factors that affect skilled typing and data entry. The information presented should apply equally well to typewriter and computer keyboards. Some data also apply to telephones and other specialized keypads used for data entry tasks. Proponents of the best-publicized alternatives to the standard keyboard have generally failed to provide convincing empirical cases for their wholesale replacement of the standard, although they might see reasonable application in certain special settings. A well-designed standard keyboard is an extremely effective data-entry device and will probably remain a key component in human-computer interaction for the foreseeable future.
From PDAs to cell phones to MP3 players, handheld electronic devices are ubiquitous. Human factors engineers and designers have a need to remain informed about advances in research on user interface design for this class of devices. This review provides human factors research summaries and research-based guidelines for the design of handheld devices. The major topics include anthropometry (fitting the device to the hand), input (types of device control and methods for data entry), output (display design), interaction design (one-handed use, scrolling, menu design, image manipulation, and using the mobile Web), and data sharing (among users, devices, and networks). Thus, this review covers the key aspects of the design of handheld devices, from the design of the physical form of the device through its hardware and software, including its behavior in networks.
Five-key text entry methods are useful for limited text entry on small devices. They use four directional keys to move a selector over an on-screen keyboard and an Enter key for selection. Although other researchers have described five-key character layouts using alphabetical order and predictive layouts based on digraph frequencies, there is considerable latitude in designing the rest of a comprehensive on-screen keyboard. Furthermore, it might be possible to capitalize on the relative strengths of the alphabetic and predictive layouts by combining them in a hybrid layout. In this paper, we describe the design factors considered and tools used to develop three five-key text-entry techniques: alphabetic, predictive, and hybrid.
SMS (short messaging service) has become extremely popular in Europe. It allows users to send and receive short asynchronous messages at low cost. Text-messaging requires text-entry facilities, usually awkward and small 12-key phone keyboards labelled with the alphabet in the classic phone-layout. As devices are becoming smaller, for example wristwatches with Internet connectivity, there is less space for keys. Researchers have experimented with three-key text input devices and come up with designs allowing text to be entered with nearly as few as 4 keystrokes per characters on average. However, experiments have shown that these interfaces are hard to learn and thus slow to use in practice. In this paper we study four three-key mobile text-entry strategies with different characteristics, namely multiring, three-based binary and multitap. Three of the methods require from 1 to 7 keystrokes per character, and the third method always retrieves the required character with a constant of only three keystrokes per character for English text. Experiments are used to demonstrate that the techniques are easy to learn and pleasant to use. The techniques are cheap and simple to implement and therefore hold great potential for future text-enabled consumer electronics.
We present a new 4-key text entry method that, unlike most few-key methods, is gestural instead of selection-based. Importantly, its gestures mimic the writing of Roman letters for high learnability. We compare this new 4-key method to predominant 3-key and 5-key methods theoretically using KSPC and empirically using a longitudinal study of 5 subjects over 10 sessions. The study includes an evaluation of the 4-key method without any on-screen visualization— an impossible condition for the selection-based methods. Our results show that the new 4-key method is quickly learned, becoming faster than the 3-key and 5-key methods after just ~10 minutes of writing, although it produces more errors. Interestingly, removing a visualization of the gestures being made causes no detriment to the 4-key method, which is an advantage for eyes-free text entry.
This chapter discusses the measures of text entry performance. These measures, for the most part are, method-agnostic that is applicable to a variety of text entry methods. Speed is one example of a method-agnostic measure. Other measures presented in the chapter are method-specific, inherent to keyboard typing, selector movement, or gestural methods. Typing on a physical keyboard has been studied extensively. Although substitutions, insertions, and omissions characterize a large percentage of typing errors, other errors also appear frequently in typing. These are high-level errors that are unlikely to occur in short transcription typing, but it may occur when participants are composing text or transcribing long documents. Most studies that analyze these types of errors simply report the number or rate of occurrences and give salient examples. Selection-based methods are those that show on-screen options, such as letters in a virtual keyboard, and allow users to select from among them. Selections may be performed directly, such as when tapping with a stylus or clicking with a mouse, or indirectly, when moving a selector with directional arrow keys and entering the highlighted letter by pressing a separate select button.
This chapter focuses on specific models of human movement and user interaction with devices. It discusses the nature of models, what they are, and why they are important tools for the research and development of human-computer interfaces. Two models are presented in detail-Fitts' model of the information-processing capability of the human motor system, and Guiard's model of bimanual control. Both Fitts' law and Guiard's model of bimanual skill are popular research tools in human-computer interactions. They are useful because they provide a simplification of the complex interactions between humans and computers. This simplification allows designers to understand and anticipate the impact of a design in a meaningful context. A founding principle of the field of human-computer interaction is that it is multidisciplinary. The field combines work in other disciplines, most notably psychology, cognitive science, and sociology. Both are widely applicable to many disciplines.
Text input for mobile or handheld devices is a flourishing research area. This article begins with a brief history of the emergence and impact of mobile com-puters and mobile communications devices. Key factors in conducting sound evaluations of new technologies for mobile text entry are presented, including methodology and experiment design. Important factors to consider are identi-fied and elaborated, such as focus of attention, text creation versus text copy tasks, novice versus expert performance, quantitative versus qualitative mea-sures, and the speed–accuracy trade-off. An exciting area within mobile text en-try is the combined use of Fitts' law and a language corpus to model, and subsequently optimize, a text entry technique. The model is described, along with examples for a variety of soft keyboards as well as the telephone keypad. A survey of mobile text entry techniques, both in research papers and in commer-cial products, is presented.
The design and evaluation of a high performance soft keyboardfor mobile systems are described. Using a model to predict theupper-bound text entry rate for soft keyboards, we designed akeyboard layout with a predicted upper-bound entry rate of 58.2wpm. This is about 35% faster than the predicted rate for a QWERTYlayout. We compared our design (OPTI) with a QWERTY layout in alongitudinal evaluation using five participants and 20 45-minutesessions of text entry. Average entry rates for OPT1 increased from17.0 wpm initially to 44.3 wpm at session 20. The average ratesexceeded those for the QWERTY layout after the 10 session (about 4hours of practice). A regression equation (R = .997) in the form ofthe power-law of learning predicts that our upper-bound predictionwould be reach at about session 50.