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30
Usability guided key-target resizing for soft keyboards
- Proc. IUI '10. ACM
, 2010
"... Soft keyboards offer touch-capable mobile and tabletop devices many advantages such as multiple language support and space for larger graphical displays. On the other hand, because soft keyboards lack haptic feedback, users often produce more typing errors. In order to make soft keyboards more robus ..."
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Cited by 24 (2 self)
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Soft keyboards offer touch-capable mobile and tabletop devices many advantages such as multiple language support and space for larger graphical displays. On the other hand, because soft keyboards lack haptic feedback, users often produce more typing errors. In order to make soft keyboards more robust to noisy input, researchers have developed keytarget resizing algorithms, where underlying target areas for keys are dynamically resized based on their probabilities. In this paper, we describe how overly aggressive key-target resizing can sometimes prevent users from typing their desired text, violating basic user expectations about keyboard functionality. We propose an anchored key-target method which aims to provide an input method that is robust to errors while respecting usability principles. In an empirical evaluation, we found that using anchored dynamic key-targets significantly reduce keystroke errors as compared to the state-ofthe-art. Author Keywords source-channel key-target resizing, language model, touch model
Dynamics and Probabilistic Text Entry
, 2003
"... We present a gestural interface for entering text on a mobile device via continuous movements, with control based on feedback from a probabilistic language model. Text is represented by continuous trajectories over a hexagonal tessellation, and entry becomes a manual control task. The language mo ..."
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Cited by 14 (9 self)
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We present a gestural interface for entering text on a mobile device via continuous movements, with control based on feedback from a probabilistic language model. Text is represented by continuous trajectories over a hexagonal tessellation, and entry becomes a manual control task. The language model is used to infer user intentions and provide predictions about future actions, and the local dynamics adapt to reduce effort in entering probable text. This leads to an interface with a stable layout, aiding user learning, but which appropriately supports the user via the probability model. Experimental results demonstrate that the application of this technique reduces variance in gesture trajectories, and is competitive in terms of throughput for mobile devices. This paper provides a practical example of a user interface making uncertainty explicit to the user, and probabilistic feedback from hypothesised goals has general application in many gestural interfaces, and is well-suited to support multimodal interaction.
Text Text Revolution: A Game that Improves Text Entry on Mobile Touchscreen Keyboards
"... Abstract. Mobile devices often utilize touchscreen keyboards for text input. However, due to the lack of tactile feedback and generally small key sizes, users often produce typing errors. Key-target resizing, which dynamically adjusts the underlying target areas of the keys based on their probabilit ..."
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Cited by 13 (0 self)
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Abstract. Mobile devices often utilize touchscreen keyboards for text input. However, due to the lack of tactile feedback and generally small key sizes, users often produce typing errors. Key-target resizing, which dynamically adjusts the underlying target areas of the keys based on their probabilities, can significantly reduce errors, but requires training data in the form of touch points for intended keys. In this paper, we introduce Text Text Revolution (TTR), a game that helps users improve their typing experience on mobile touchscreen keyboards in three ways: first, by providing targeting practice, second, by highlighting areas for improvement, and third, by generating ideal training data for key-target resizing as a side effect of playing the game. In a user study, participants who played 20 rounds of TTR not only improved in accuracy over time, but also generated useful data for key-target resizing. To demonstrate usefulness, we trained key-target resizing on touch points collected from the first 10 rounds, and simulated how participants would have performed had personalized key-target resizing been used in the second 10 rounds. Key-target resizing reduced errors by 21.4%.
A Keystroke and Pointer Control Input Interface for Wearable Computers
"... The widespread adoption of mobile electronic devices and the advent of wearable computing has encouraged the development of compact alternatives to the keyboard and mouse. These include one-handed keyboards, digitizing tablets, and glove-based devices. This paper describes a combination pointer posi ..."
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Cited by 7 (0 self)
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The widespread adoption of mobile electronic devices and the advent of wearable computing has encouraged the development of compact alternatives to the keyboard and mouse. These include one-handed keyboards, digitizing tablets, and glove-based devices. This paper describes a combination pointer position and non-chorded keystroke input device that relies on miniature wrist-worn wireless video cameras that track finger position. A Hidden Markov Model is used to correlate finger movements to keystrokes during a brief training phase, after which the user can type in the air or above a flat surface as if typing on a standard keyboard. Language statistics are used to help disambiguate keystrokes, allowing the assignment of multiple unique keys to each finger and obviating chorded input. In addition, the system can be trained to recognize certain finger positions for switching between input modes; for example, from typing mode to pointer movement mode. In the latter mode of operation, the position of the mouse pointer is controlled by hand movement. The camera motion is estimated by tracking environmental features and is used to control pointer position. This allows fast switching between keystroke mode and pointer control mode. 1
Fast Finger Tracking System for In-Air Typing Interface
- Proc. CHI EA ’09
"... We developed a system which performs 3D motion tracking of human’s hand and fingers from images of a single high-frame-rate camera and that recognizes his/her typing motion in the air. Our template-matching-based method using hand textures reduces background effect and enables markerless tracking. I ..."
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Cited by 6 (0 self)
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We developed a system which performs 3D motion tracking of human’s hand and fingers from images of a single high-frame-rate camera and that recognizes his/her typing motion in the air. Our template-matching-based method using hand textures reduces background effect and enables markerless tracking. In addition, use of a high-frame-rate camera enables recognition of rapid typing motion which is difficult to track using standard cameras. In order to realize real-time recognition, we developed hardware which parallelizes and accelerates image processing. As a result, we achieved real-time recognition of typing motion with the throughput of 138 fps (frames per second) and the latency of 29 ms.
The Postural Comfort Zone for Reaching Gestures
- in HFES Annual Meeting Notes
, 2003
"... We have proposed a method for objective assessment of postural comfort (Kölsch et al., 2003). We defined comfort as the range of postures that is voluntarily assumed despite the availability of other postures. Designing user interfaces within the limits of comfort zones can avert risks associated wi ..."
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Cited by 6 (1 self)
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We have proposed a method for objective assessment of postural comfort (Kölsch et al., 2003). We defined comfort as the range of postures that is voluntarily assumed despite the availability of other postures. Designing user interfaces within the limits of comfort zones can avert risks associated with unknown alternative use patters of the interface. Here we report on a user study that investigated the comfort zone for free-hand gestures in the horizontal plane at about stomach height. This space is of particular interest to novel technologies such as gesture recognition and virtual reality. The results are in line with previous studies on postural discomfort, but improve on resolution and are not based on subjective, questionnaire-based data acquisition. This study also serves as an example for how to design studies for comfort evaluation.
Multimodal Text Entry on Mobile Devices
"... Text entry on mobile phones is growing in popularity due to increasing use of applications such as SMS and e-mail. Mobile phones and mobile devices in general do not have a keyboard which is as convenient as that on the desktop computers. Mobile phones in particular tend to have only a numeric keypa ..."
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Cited by 4 (1 self)
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Text entry on mobile phones is growing in popularity due to increasing use of applications such as SMS and e-mail. Mobile phones and mobile devices in general do not have a keyboard which is as convenient as that on the desktop computers. Mobile phones in particular tend to have only a numeric keypad on which multiple letters map to the same key. Users currently use methods such as multi-tap (“this ” = 8 44 444 7777) or Tegic Communications ’ T9 (“this ” = 8447) to enter text using a numeric keypad. Novice users of these methods achieve text entry rates of 5-10 words per minute [1]. 2 Multi-modal combination of speech & keypad Speech has a high communication bandwidth which is estimated at about 250 words per minute [2]. However, text entry throughput using automatic speech recognition (ASR) is much lower in practice due to the time spent by the user in checking for and correcting the ASR errors which are inevitable with the current state of the art ASR systems. We will demonstrate a system which uses a combination of speech and keypad input to improve the overall text entry experience for the user of the mobile devices. 3 Overview of operation
Ubiquitous keyboard for small mobile devices: Harnessing multipath fading for fine-grained keystroke localization
- In Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys ’14
, 2014
"... A well-known bottleneck of contemporary mobile devices is the inefficient and error-prone touchscreen keyboard. In this paper, we propose UbiK, an alternative portable text-entry method that allows user to make keystrokes on conventional surfaces, e.g., wood desktop. UbiK enables text-input ex-perie ..."
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Cited by 4 (1 self)
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A well-known bottleneck of contemporary mobile devices is the inefficient and error-prone touchscreen keyboard. In this paper, we propose UbiK, an alternative portable text-entry method that allows user to make keystrokes on conventional surfaces, e.g., wood desktop. UbiK enables text-input ex-perience similar to that on a physical keyboard, but it only requires a keyboard outline printed on the surface or a piece of paper atop. The core idea is to leverage the microphone on a mobile device to accurately localize the keystrokes. To achieve fine-grained, centimeter scale granularity, UbiK ex-tracts and optimizes the location-dependent multipath fad-ing features from the audio signals, and takes advantage of the dual-microphone interface to improve signal diversity. We implement UbiK as an Android application. Our exper-iments demonstrate that UbiK is able to achieve above 95% of localization accuracy. Field trial involving first-time users shows that UbiK can significantly improve text-entry speed over current on-screen keyboards.
A Virtual Keyboard Based on True-3D Optical Ranging
"... In this paper, a complete system is presented which mimics a QWERTY keyboard on an arbitrary surface. The system consists of a pattern projector and a true-3D range camera for detecting the typing events. We exploit depth information acquired with the 3D range camera and detect the hand region using ..."
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Cited by 3 (0 self)
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In this paper, a complete system is presented which mimics a QWERTY keyboard on an arbitrary surface. The system consists of a pattern projector and a true-3D range camera for detecting the typing events. We exploit depth information acquired with the 3D range camera and detect the hand region using a pre-computed reference frame. The fingertips are found by analyzing the hands ’ contour and fitting the depth curve with different feature models. To detect a keystroke, we analyze the feature of the depth curve and map it back to a global coordinate system to find which key was pressed. These steps are fully automated and do not require human intervention. The system can be used in any application requiring zero form factor and minimized or no contact with a medium, as in a large number of cases in human-to-computer interaction, virtual reality, game control, 3D designs, etc.
A Survey of Manual Input Devices
, 2007
"... This paper provides a brief survey of direct and indirect input devices and their performance characteristics. It describes the design and applicability of physical and soft keyboards, touch screens, mice, isotonic and isometric joysticks, touchpads, and trackballs. It concludes with a taxonomy of t ..."
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Cited by 2 (1 self)
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This paper provides a brief survey of direct and indirect input devices and their performance characteristics. It describes the design and applicability of physical and soft keyboards, touch screens, mice, isotonic and isometric joysticks, touchpads, and trackballs. It concludes with a taxonomy of the input devices based on their usefulness in mobile settings.
