scholarly journals Controller-based Text-input Techniques for Virtual Reality: An Empirical Comparison

2019 ◽  
Vol 19 (3) ◽  
Author(s):  
Costas Boletsis ◽  
Stian Kongsvik
Keyword(s):  
User Experience ◽  
Empirical Evaluation ◽  
Structured Interview ◽  
Error Rates ◽  
Text Entry ◽  
Empirical Comparison ◽  
Entry Rate ◽  
Future Studies ◽  
Text Input ◽  
Input Techniques

Existing consumer VR systems support text input using handheld controllers in combination with virtual keyboards and many designers have attempted to build on these widely used techniques. However, information on current and well-established VR text-input techniques is lacking. In this work, we conduct a comparative empirical evaluation of four controller-based VR text-input techniques, namely, raycasting, drum-like keyboard, head-directed input, and split keyboard. We focus on their text-entry rate and accuracy, usability, and user experience. Twenty-two participants evaluated the techniques by completing a typing session, answering usability and user-experience questionnaires, and participating in a semi-structured interview. The drum-like keyboard and the raycasting techniques stood out, achieving good usability scores, positive experiential feedback, satisfactory text-entry rates, and moderate error rates that can be reduced in future studies. The specific documented usability and experiential characteristics of the techniques are presented and discussed herein.

scholarly journals Text Input in Virtual Reality: A Preliminary Evaluation of the Drum-Like VR Keyboard

Technologies ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 31 ◽  
Author(s):  
Costas Boletsis ◽  
Stian Kongsvik
Keyword(s):  
Virtual Reality ◽  
User Experience ◽  
Total Error ◽  
Preliminary Evaluation ◽  
Text Entry ◽  
Entry Rate ◽  
Drum Set ◽  
Text Input ◽  
Total Error Rate ◽  
Experience Questionnaire

The drum-like virtual reality (VR) keyboard is a contemporary, controller-based interface for text input in VR that uses a drum set metaphor. The controllers are used as sticks which, through downward movements, “press” the keys of the virtual keyboard. In this work, a preliminary feasibility study of the drum-like VR keyboard is described, focusing on the text entry rate and accuracy as well as its usability and the user experience it offers. Seventeen participants evaluated the drum-like VR keyboard by having a typing session and completing a usability and a user experience questionnaire. The interface achieved a good usability score, positive experiential feedback around its entertaining and immersive qualities, a satisfying text entry rate (24.61 words-per-minute), as well as moderate-to-high total error rate (7.2%) that can probably be further improved in future studies. The work provides strong indications that the drum-like VR keyboard can be an effective and entertaining way to type in VR.

scholarly journals Text Input Techniques for Immersive Virtual Environments: An Empirical Comparison

2002 ◽  
Vol 46 (26) ◽  
pp. 2154-2158 ◽  
Author(s):  
Doug A. Bowman ◽  
Christopher J. Rhoton ◽  
Marcio S. Pinho
Keyword(s):  
Virtual Environments ◽  
Task Performance ◽  
User Satisfaction ◽  
Empirical Evaluation ◽  
Empirical Comparison ◽  
Immersive Virtual Environments ◽  
Text Input ◽  
Input Techniques ◽  
Single Technique

Symbolic input, including text and numeric input, can be an important user task in applications of virtual environments (VEs). However, very little research has been performed to support this task in immersive VEs. This paper presents the results of an empirical evaluation of four text input techniques for immersive VEs. The techniques include the Pinch Keyboard (a typing emulation technique using pinch gloves), a one-hand chord keyboard, a soft keyboard using a pen & tablet, and speech. The experiment measured both task performance and usability characteristics of the four techniques. Results indicate that the speech technique is the fastest, while the pen & tablet keyboard produces the fewest errors. However, no single technique exhibited high levels of performance, usability and user satisfaction.

A study on text entry methods based on eye gestures

2015 ◽  
Vol 9 (1) ◽  
pp. 48-67 ◽  
Author(s):  
Marco Porta
Keyword(s):  
Learning Process ◽  
Text Entry ◽  
User Performance ◽  
Content Type ◽  
Virtual Keyboard ◽  
Text Input ◽  
Learning Procedure ◽  
Proper Training ◽  
Screen Space ◽  
Input Techniques

Purpose – The purpose of this paper is to consider the two main existing text input techniques based on “eye gestures” – namely EyeWrite and Eye-S – and compare them to each other and to the traditional “virtual keyboard” approach. Design/methodology/approach – The study primarily aims to assess user performance at the very beginning of the learning process. However, a partial longitudinal evaluation is also provided. Two kinds of experiments have been implemented involving 14 testers. Findings – Results show that while the virtual keyboard is faster, EyeWrite and Eye-S are also appreciated and can be viable alternatives (after a proper training period). Practical implications – Writing methods based on eye gestures deserve special attention, as they require less screen space and need limited tracking precision. This study highlights the fact that gesture-based techniques imply a greater initial effort, and require proper training not only to gain knowledge of eye interaction per se, but also for learning the gesture alphabet. The author thinks that the investigation can drive the designers of gaze-controlled writing techniques based on gestures to put more consideration on the intuitiveness of gestures themselves, as they may greatly influence user performance in the first stages of the learning process. Originality/value – This is the first study comparing EyeWrite and Eye-S. Moreover, unlike other analyses, the investigation is mainly aimed at assessing user performance with the three text entry methods at the inception of the learning procedure.

scholarly journals Augmenting Around-Device Interaction by Geomagnetic Field Built-in Sensor Utilization

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3087
Author(s):  
Sandi Ljubic ◽  
Franko Hržić ◽  
Alen Salkanovic ◽  
Ivan Štajduhar
Keyword(s):  
Magnetic Field ◽  
Neural Networks ◽  
Curve Fitting ◽  
Absolute Error ◽  
Interaction Space ◽  
Text Entry ◽  
Proof Of Concept ◽  
Field Sensor ◽  
Device Interaction ◽  
Input Techniques

In this paper, we investigate the possibilities for augmenting interaction around the mobile device, with the aim of enabling input techniques that do not rely on typical touch-based gestures. The presented research focuses on utilizing a built-in magnetic field sensor, whose readouts are intentionally affected by moving a strong permanent magnet around a smartphone device. Different approaches for supporting magnet-based Around-Device Interaction are applied, including magnetic field fingerprinting, curve-fitting modeling, and machine learning. We implemented the corresponding proof-of-concept applications that incorporate magnet-based interaction. Namely, text entry is achieved by discrete positioning of the magnet within a keyboard mockup, and free-move pointing is enabled by monitoring the magnet’s continuous movement in real-time. The related solutions successfully expand both the interaction language and the interaction space in front of the device without altering its hardware or involving sophisticated peripherals. A controlled experiment was conducted to evaluate the provided text entry method initially. The obtained results were promising (text entry speed of nine words per minute) and served as a motivation for implementing new interaction modalities. The use of neural networks has shown to be a better approach than curve fitting to support free-move pointing. We demonstrate how neural networks with a very small number of input parameters can be used to provide highly usable pointing with an acceptable level of error (mean absolute error of 3 mm for pointer position on the smartphone display).

scholarly journals Combining Multiple Organizational-level Databases: An Empirical Evaluation of Different Matching Methods

2021 ◽  
pp. 004912412098618
Author(s):  
Tim de Leeuw ◽  
Steffen Keijl
Keyword(s):  
Empirical Evaluation ◽  
Organizational Level ◽  
Random Errors ◽  
Data Set ◽  
Future Studies ◽  
Matching Methods ◽  
Matching Process ◽  
Significant Relationships ◽  
Research Findings ◽  
Insight Into

Although multiple organizational-level databases are frequently combined into one data set, there is no overview of the matching methods (MMs) that are utilized because the vast majority of studies does not report how this was done. Furthermore, it is unclear what the differences are between the utilized methods, and it is unclear whether research findings might be influenced by the utilized method. This article describes four commonly used methods for matching databases and potential issues. An empirical comparison of those methods used to combine regularly used organizational-level databases reveals large differences in the number of observations obtained. Furthermore, empirical analyses of these different methods reveal that several of them produce both systematic and random errors. These errors can result in erroneous estimations of regression coefficients in terms of direction and/or size as well as an issue where truly significant relationships might be found to be insignificant. This shows that research findings can be influenced by the MM used, which would argue in favor of the establishment of a preferred method as well as more transparency on the utilized method in future studies. This article provides insight into the matching process and methods, suggests a preferred method, and should aid researchers, reviewers, and editors with both combining multiple databases and describing and assessing them.

scholarly journals Five Challenges for Intelligent Text Entry Methods

AI Magazine ◽  
2009 ◽  
Vol 30 (4) ◽  
pp. 85 ◽  
Author(s):  
Per Ola Kristensson
Keyword(s):  
Error Correction ◽  
Effective Means ◽  
Historical Context ◽  
Research Literature ◽  
Error Rates ◽  
Localization Error ◽  
Text Entry ◽  
Context Of Use ◽  
Selection Of

For text entry methods to be useful they have to deliver high entry rates and low error rates. At the same time they need to be easy-to-learn and provide effective means of correcting mistakes. Intelligent text entry methods combine AI techniques with HCI theory to enable users to enter text as efficiently and effortlessly as possible. Here I sample a selection of such techniques from the research literature and set them into their historical context. I then highlight five challenges for text entry methods that aspire to make an impact in our society: localization, error correction, editor support, feedback, and context of use.

Typing on Tap: Estimating a Finger-Worn One-Handed Chording Keyboard’s Text Entry Rate

2021 ◽  
Author(s):  
Jason Tu ◽  
Angeline Vidhula Jeyachandra ◽  
Deepthi Nagesh ◽  
Naresh Prabhu ◽  
Thad Starner
Keyword(s):  
Text Entry ◽  
Entry Rate

Pickup Usability Dominates

2011 ◽  
pp. 42-59 ◽  
Author(s):  
Mark David Dunlop ◽  
Michelle Montgomery Masters
Keyword(s):  
Mobile Phone ◽  
Mobile Devices ◽  
Touch Screen ◽  
Theoretical Modelling ◽  
Text Entry ◽  
Full Size ◽  
Entry Speed ◽  
Research Challenge ◽  
Input Techniques ◽  
Initial Results

Text entry on mobile devices (e.g. phones and PDAs) has been a research challenge since devices shrank below laptop size: mobile devices are simply too small to have a traditional full-size keyboard. There has been a profusion of research into text entry techniques for smaller keyboards and touch screens: some of which have become mainstream, while others have not lived up to early expectations. As the mobile phone industry moves to mainstream touch screen interaction we will review the range of input techniques for mobiles, together with evaluations that have taken place to assess their validity: from theoretical modelling through to formal usability experiments. We also report initial results on iPhone text entry speed.

scholarly journals Consensify: A Method for Generating Pseudohaploid Genome Sequences from Palaeogenomic Datasets with Reduced Error Rates

Genes ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 50
Author(s):  
Axel Barlow ◽  
Stefanie Hartmann ◽  
Javier Gonzalez ◽  
Michael Hofreiter ◽  
Johanna L. A. Paijmans
Keyword(s):  
Clustering Analysis ◽  
Error Rates ◽  
Sequencing Error ◽  
Genome Sequences ◽  
High Quality ◽  
Short Read ◽  
Future Studies ◽  
Sequencing Coverage ◽  
Simplifying Assumptions ◽  
Population Clustering

A standard practise in palaeogenome analysis is the conversion of mapped short read data into pseudohaploid sequences, frequently by selecting a single high-quality nucleotide at random from the stack of mapped reads. This controls for biases due to differential sequencing coverage, but it does not control for differential rates and types of sequencing error, which are frequently large and variable in datasets obtained from ancient samples. These errors have the potential to distort phylogenetic and population clustering analyses, and to mislead tests of admixture using D statistics. We introduce Consensify, a method for generating pseudohaploid sequences, which controls for biases resulting from differential sequencing coverage while greatly reducing error rates. The error correction is derived directly from the data itself, without the requirement for additional genomic resources or simplifying assumptions such as contemporaneous sampling. For phylogenetic and population clustering analysis, we find that Consensify is less affected by artefacts than methods based on single read sampling. For D statistics, Consensify is more resistant to false positives and appears to be less affected by biases resulting from different laboratory protocols than other frequently used methods. Although Consensify is developed with palaeogenomic data in mind, it is applicable for any low to medium coverage short read datasets. We predict that Consensify will be a useful tool for future studies of palaeogenomes.

An In-Depth Look into the Text Entry User Experience on the iPhone

2008 ◽  
Vol 52 (5) ◽  
pp. 508-512 ◽  
Author(s):  
Jennifer M. Allen ◽  
Leslie A. McFarlin ◽  
Thomas Green
Keyword(s):  
User Experience ◽  
Text Entry
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