EOG-Based Ocular and Gaze Angle Estimation Using an Extended Kalman Filter

Barbara, Nathaniel; Camilleri, Tracey A.; Camilleri, Kenneth P.

doi:10.1145/3379156.3391357

Cited by 8 publications

(7 citation statements)

References 8 publications

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“…The mean absolute error was about 2.42° across 6 subjects. Further, an extended Kalman filter has been applied in [16] to improve the accuracy. The third method, machine learning models, has shown great potential in eye movement detection.…”

Section: Introductionmentioning

confidence: 99%

A Robust Gaze Estimation Approach via Exploring Relevant Electrooculogram Features and Optimal Electrodes Placements

Zeng,

Tao,

Zhu

et al. 2024

IEEE J. Transl. Eng. Health Med.

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Gaze estimation, as a technique that reflects individual attention, can be used for disability assistance and assisting physicians in diagnosing diseases such as autism spectrum disorder (ASD), Parkinson’s disease, and attention deficit hyperactivity disorder (ADHD). Various techniques have been proposed for gaze estimation and achieved high resolution. Among these approaches, electrooculography (EOG)-based gaze estimation, as an economical and effective method, offers a promising solution for practical applications. Objective: In this paper, we systematically investigated the possible EOG electrode locations which are spatially distributed around the orbital cavity. Afterward, quantities of informative features to characterize physiological information of eye movement from the temporal-spectral domain are extracted from the seven differential channels. Methods and procedures: To select the optimum channels and relevant features, and eliminate irrelevant information, a heuristical search algorithm (i.e., forward stepwise strategy) is applied. Subsequently, a comparative analysis of the impacts of electrode placement and feature contributions on gaze estimation is evaluated via 6 classic models with 18 subjects. Results: Experimental results showed that the promising performance was achieved both in the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) within a wide gaze that ranges from −50° to +50°. The MAE and RMSE can be improved to 2.80° and 3.74° ultimately, while only using 10 features extracted from 2 channels. Compared with the prevailing EOG-based techniques, the performance improvement of MAE and RMSE range from 0.70° to 5.48° and 0.66° to 5.42°, respectively. Conclusion: We proposed a robust EOG-based gaze estimation approach by systematically investigating the optimal channel/feature combination. The experimental results indicated not only the superiority of the proposed approach but also its potential for clinical application. Clinical and translational impact statement: Accurate gaze estimation is a key step for assisting disabilities and accurate diagnosis of various diseases including ASD, Parkinson’s disease, and ADHD. The proposed approach can accurately estimate the points of gaze via EOG signals, and thus has the potential for various related medical applications.

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Section: Introductionmentioning

confidence: 99%

A Robust Gaze Estimation Approach via Exploring Relevant Electrooculogram Features and Optimal Electrodes Placements

Zeng,

Tao,

Zhu

et al. 2024

IEEE J. Transl. Eng. Health Med.

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“…Diverse KF state estimators have been developed, drawing upon the mechanical [ 10 , 20 , 29 ], electrical [ 30 , 31 ], and parametric [ 15 , 32 ] attributes of the eye and its ocular movements. Notably, lumped-element-based dynamic models have served as foundational state estimators in this context [ 10 , 20 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

“…While most of the model-based techniques have demonstrated proficiency in addressing issues such as the elimination of eye blinks, offset correction, and signal denoising, they have typically necessitated real-time operation facilitated by a brain or neural controller [ 10 , 29 ]. Similarly, electrical models rooted in Coulomb’s law have been developed to rectify the baseline drift in EOG signals [ 25 , 30 ], but they encounter comparable challenges in preserving the integrity of EOG saccades during the denoising process. In VOG systems, KF fusion-based methodologies have been employed to denoise ocular motion signals derived from pupil reflections [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography

Gunawardane,

MacNeil,

Zhao

et al. 2024

Sensors

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Electrooculography (EOG) serves as a widely employed technique for tracking saccadic eye movements in a diverse array of applications. These encompass the identification of various medical conditions and the development of interfaces facilitating human–computer interaction. Nonetheless, EOG signals are often met with skepticism due to the presence of multiple sources of noise interference. These sources include electroencephalography, electromyography linked to facial and extraocular muscle activity, electrical noise, signal artifacts, skin-electrode drifts, impedance fluctuations over time, and a host of associated challenges. Traditional methods of addressing these issues, such as bandpass filtering, have been frequently utilized to overcome these challenges but have the associated drawback of altering the inherent characteristics of EOG signals, encompassing their shape, magnitude, peak velocity, and duration, all of which are pivotal parameters in research studies. In prior work, several model-based adaptive denoising strategies have been introduced, incorporating mechanical and electrical model-based state estimators. However, these approaches are really complex and rely on brain and neural control models that have difficulty processing EOG signals in real time. In this present investigation, we introduce a real-time denoising method grounded in a constant velocity model, adopting a physics-based model-oriented approach. This approach is underpinned by the assumption that there exists a consistent rate of change in the cornea-retinal potential during saccadic movements. Empirical findings reveal that this approach remarkably preserves EOG saccade signals, resulting in a substantial enhancement of up to 29% in signal preservation during the denoising process when compared to alternative techniques, such as bandpass filters, constant acceleration models, and model-based fusion methods.

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“…This paper is set to be used for detecting and estimating one's eye's position in frames of a film by using a multi-model Kalman filter algorithm. In the presented method, fixed velocity and fixed acceleration models of the multi-model Kalman filter are employed due to the dynamics of the target (eye) [18][19][20]. In the second section, pupillary tracking based on the Kalman filter and its equations is explained.…”

Section: Introductionmentioning

confidence: 99%

Eye Tracking Algorithm Based on Multi Model Kalman Filter

Bagherzadeh

Toosizadeh

2022

HighTech. Innov. J.

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One of the most important pieces of Human Machine Interface (HMI) equipment is an eye tracking system that is used for many different applications. This paper aims to present an algorithm in order to improve the efficiency of eye tracking in the image by means of a multi-model Kalman filter. In the classical Kalman filter, one model is used for estimation of the object, but in the multi-model Kalman filter, several models are used for estimating the object. The important features of the multiple-model Kalman filter are improving the efficiency and reducing its estimating errors relative to the classical Kalman filter. The proposed algorithm consists of two parts. The first step is recognizing the initial position of the eye, and Support Vector Machine (SVM) has been used in this part. In the second part, the position of the eye is predicted in the next frame by using a multi-model Kalman filter, which applies constant speed and acceleration models based on the normal human eye. Doi: 10.28991/HIJ-2022-03-01-02 Full Text: PDF

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EOG-Based Ocular and Gaze Angle Estimation Using an Extended Kalman Filter

Cited by 8 publications

References 8 publications

A Robust Gaze Estimation Approach via Exploring Relevant Electrooculogram Features and Optimal Electrodes Placements

A Robust Gaze Estimation Approach via Exploring Relevant Electrooculogram Features and Optimal Electrodes Placements

A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography

Eye Tracking Algorithm Based on Multi Model Kalman Filter

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