Typing with eye-gaze and tooth-clicks

Zhao, Xiaoyu; Guestrin, Elias D.; Sayenko, Dimitry G.; Simpson, Tyler; Gauthier, Michel; Popović, Miloš R.

doi:10.1145/2168556.2168632

Cited by 26 publications

(11 citation statements)

References 18 publications

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“…Growing computational power makes it possible to analyze spoken words and even video recordings online. So speech recognition [13,14] or eye tracking [15][16][17][18] have emerged as alternatives. Eye tracking for text entry amounts to moving a mouse pointer across an on-screen keyboard.…”

Section: Related Workmentioning

confidence: 99%

Efficient computer operation for users with a neuromuscular disease with OnScreenDualScribe

2014

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We developed a tool based on a modified number pad to empower persons with certain diseases, in particular of neuromuscular origin, to efficiently operate a computer and enter text. As the keypad lies securely in both hands, the system is ideal for someone who has motor problems using a full-size keyboard. The software offers various assistive techniques. For example, text entry is facilitated with the help of word prediction, and an ambiguous mode with word-level disambiguation allows text entry using the entire Latin alphabet with six keys. In addition to describing the system, we analyze the ambiguous mode and the influence of dictionary size. Initial empirical results with the system, which is already in operation, indicate that it indeed represents a viable alternative by decreasing effort without increasing the time to operate a computer. This journal article mainly differs from the related proceedings paper through an extended literature review and analyses regarding dictionary size.

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Section: Related Workmentioning

confidence: 99%

Efficient computer operation for users with a neuromuscular disease with OnScreenDualScribe

2014

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“…Object acquisition refers to the movement of focus/cursor over the object to be selected and object selection means activation of a selection trigger when the focus/cursor comes over the desired object. Some of the techniques used for object acquisition are: eye gaze tracking [1], [2], face tracking [3], [4], facial feature tracking [5], scanning [6]- [9], and tongue movement [10]. Object selection can be performed by using key trigger [11], eye blinking [12], [13], dwell time trigger [14], [15], antisaccades, gaze gestures, on-off screen buttons, dashers, pEYEs [16], mouth opening click [17], tooth clicker [2], brows up clicker [8], EMG clicking [18], and clicking with smiling [19].…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Object Acquisition Using Automatic Scanning and Facial Feature Tracking

Singh

2018

ijarcs

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This paper presents a comparison of object acquisition in an HCI system using two different techniques: automatic scanning and facial feature tracking. In automatic scanning the focus moves from one object to next object automatically after a predefined time period called scanning time. Automatic scanning has been implemented by using MATLAB algorithm which virtually activates the tab key after each scanning time and the focus moves from object to object. The user activates a selection trigger for selection of the object when the focus comes over the object of interest. Whereas, in facial feature tracking approach the mouse cursor is moved in proportion to the movement of user's face. To implement this technique Camera Mouse has been used which requires a simple webcam. It continuously takes facial images of the user and finds the mouse cursor position from the face coordinates. The two techniques are compared based upon accuracy and acquisition time for acquisition of text and graphic objects.

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“…While interacting with a computer using an eye tracker, eye movements may be used to control the position of the pointer. Selections of targets on the screen can then be performed by blinking an eye (e.g., Ashtiani and MacKenzie [2010] and Tangsuksant et al [2012]), pushing a physical button (e.g., MacKenzie and Zhang [2008]), or moving muscles that can still be controlled (e.g., Zhao et al [2012]). However, these solutions for performing target selections do not suit all users and may cease to be viable options for many users because of their declining physical capabilities.…”

Section: Introductionmentioning

confidence: 99%

Filteryedping: Design Challenges and User Performance of Dwell-Free Eye Typing

Pedrosa

Pimentel

Wright³

et al. 2015

ACM Trans. Access. Comput.

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The ability to use the movements of the eyes to write is extremely important for individuals with a severe motor disability. With eye typing, a virtual keyboard is shown on the screen and the user enters text by gazing at the intended keys one at a time. With dwell-based eye typing, a key is selected by continuously gazing at it for a specific amount of time. However, this approach has two possible drawbacks: unwanted selections and slow typing rates. In this study, we propose a dwell-free eye typing technique that filters out unintentionally selected letters from the sequence of letters looked at by the user. It ranks possible words based on their length and frequency of use and suggests them to the user. We evaluated Filteryedping with a series of experiments. First, we recruited participants without disabilities to compare it with another potential dwell-free technique and with a dwell-based eye typing interface. The results indicate it is a fast technique that allows an average of 15.95 words per minute after 100min of typing. Then, we improved the technique through iterative design and evaluation with individuals who have severe motor disabilities. This phase helped to identify and create parameters that allow the technique to be adapted to different users.

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Typing with eye-gaze and tooth-clicks

Cited by 26 publications

References 18 publications

Efficient computer operation for users with a neuromuscular disease with OnScreenDualScribe

Efficient computer operation for users with a neuromuscular disease with OnScreenDualScribe

Performance Comparison of Object Acquisition Using Automatic Scanning and Facial Feature Tracking

Filteryedping: Design Challenges and User Performance of Dwell-Free Eye Typing

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