EyeTell: Tablet-based Calibration-free Eye-typing using Smooth-pursuit movements

Bafna, Tanya; Bækgaard, Per; Hansen, John Paulin

doi:10.1145/3448018.3458015

Cited by 8 publications

(9 citation statements)

References 18 publications

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“…The distance between the users and the iPad screen was maintained by mounting the iPad to a stand, which was fixed to the table. The application EyeTell and its working performance has been described in further detail in Bafna et al ( 2 ).…”

Section: Methodsmentioning

confidence: 99%

“…EyeTell (Bafna et al, 2021) differs from most of these existing keyboards in two ways -( 1) it has been implemented on a tablet, and (2) it does not require any calibration. Other smooth-pursuit typing systems not using individual calibration have been proposed.…”

Section: Introductionmentioning

confidence: 99%

“…To select a target, it should be followed for at least 2 s with a smooth-pursuit eye movement. The process of selection as well as the key performance metrics, such as typing speed and error rate, have been depicted in (Bafna et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Zeng & Roetting ( 51 ) improved the typing system by introducing word-prediction based on a convolutional neural network, achieving a maximum typing speed of 4.5 WPM. Additional eye-typing assistive systems are detailed in Bafna et al ( 2 ).…”

Section: Introductionmentioning

confidence: 99%

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Smooth-pursuit performance during eye-typing from memory indicates mental fatigue

Bafna-Rührer¹,

Bækgaard²,

Hansen³

2022

JEMR

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Mental fatigue is known to occur as a result of activities related to e.g. transportation, health-care and military operations. Gaze tracking has wide-ranging applications, with the technology becoming more compact and processing power reducing. Though numerous techniques have been applied to measure mental fatigue using gaze tracking, smooth-pursuit movement, a natural eye movement generated when following a moving object with gaze, has not been explored with relation to mental fatigue. In this paper, we report the results from a smooth-pursuit movement based eye-typing experiment with varying task difficulty to generate cognitive load, performed in the morning and afternoon by 36 participants. We have investigated the effects of time-on-task and time of day on mental fatigue using self-reported questionnaires and smooth-pursuit performance, extracted from the gaze data. The self-reported mental fatigue increased due to time-on-task, but the time of day did not have an effect. The results illustrate that smooth-pursuit movement performance declined with time-on-task, with increased error in the gaze position and an inability to match the speed of the moving object. The findings exhibit the feasibility of mental fatigue detection using smooth-pursuit movements during an eye-interactive task of eye-typing.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Smooth-pursuit performance during eye-typing from memory indicates mental fatigue

Bafna-Rührer¹,

Bækgaard²,

Hansen³

2022

JEMR

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition to the accuracy problem, the time needed for calibration may affect a user's acceptance and experience. This motivates researchers to design applications that work without personal calibration, such as using smooth-pursuit movements based on the front-facing camera of a tablet Eyetell (Bafna, Baekgaard, & Paulin Hansen, 2021). This calibration-free design is appealing when it comes to the use of public displays.…”

Section: Gaze Estimation Using a Webcammentioning

confidence: 99%

GaVe: A webcam-based gaze vending interface using one-point calibration

Zeng

Liu

Cheng

et al. 2023

JEMR

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Gaze input, i.e., information input via eye of users, represents a promising method for contact-free interaction in human-machine systems. In this paper, we present the GazeVending interface (GaVe), which lets users control actions on a display with their eyes. The interface works on a regular webcam, available on most of today's laptops, and only requires a short one-point calibration before use. GaVe is designed in a hierarchical structure, presenting broad item cluster to users first and subsequently guiding them through another selection round, which allows the presentation of a large number of items. Cluster/item selection in GaVe is based on the dwell time, i.e., the time duration that users look at a given Cluster/item. A user study (N=22) was conducted to test optimal dwell time thresholds and comfortable human-to-display distances. Users' perception of the system, as well as error rates and task completion time were registered. We found that all participants were able to quickly understand and know how to interact with the interface, and showed good performance, selecting a target item within a group of 12 items in 6.76 seconds on average. We provide design guidelines for GaVe and discuss the potentials of the system.

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Development of a real-time eye movement-based computer interface for communication with improved accuracy for disabled people under natural head movements

Chhimpa,

Kumar,

Garhwal

et al. 2023

J Real-Time Image Proc

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EyeTell: Tablet-based Calibration-free Eye-typing using Smooth-pursuit movements

Cited by 8 publications

References 18 publications

Smooth-pursuit performance during eye-typing from memory indicates mental fatigue

Smooth-pursuit performance during eye-typing from memory indicates mental fatigue

GaVe: A webcam-based gaze vending interface using one-point calibration

Development of a real-time eye movement-based computer interface for communication with improved accuracy for disabled people under natural head movements

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