24-Gaze-Point Calibration Method for Improving the Precision of AC-EOG Gaze Estimation

Suhaimi, Muhammad Syaiful Amri bin; Matsushita, Kojiro; Sasaki, Minoru; Njeri, Waweru

doi:10.3390/s19173650

Cited by 10 publications

(7 citation statements)

References 20 publications

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“…For both OET and EOG technologies, calibration is among the most important factors in achieving accurate eye tracking, adjusting for the individual differences caused by either geometric characteristics of the subject’s eyes or glasses’ placement. 70 , 71 …”

Section: Eye-tracking Systems’ Feasibility In the ...mentioning

confidence: 99%

Detecting Soldiers’ Fatigue Using Eye-Tracking Glasses: Practical Field Applications and Research Opportunities

2021

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Introduction Objectively determining soldiers’ fatigue levels could help prevent injuries or accidents resulting from inattention or decreased alertness. Eye-tracking technologies, such as optical eye tracking (OET) and electrooculography (EOG), are often used to monitor fatigue. Eyeblinks—especially blink frequency and blink duration—are known as easily observable and valid biomarkers of fatigue. Currently, various eye trackers (i.e., eye-tracking glasses) are available on the market using either OET or EOG technologies. These wearable eye trackers offer several advantages, including unobtrusive functionality, practicality, and low costs. However, several challenges and limitations must be considered when implementing these technologies in the field to monitor fatigue levels. This review investigates the feasibility of eye tracking in the field focusing on the practical applications in military operational environments. Materials and Method This paper summarizes the existing literature about eyeblink dynamics and available wearable eye-tracking technologies, exposing challenges and limitations, as well as discussing practical recommendations on how to improve the feasibility of eye tracking in the field. Results So far, no eye-tracking glasses can be recommended for use in a demanding work environment. First, eyeblink dynamics are influenced by multiple factors; therefore, environments, situations, and individual behavior must be taken into account. Second, the glasses’ placement, sunlight, facial or body movements, vibrations, and sweat can drastically decrease measurement accuracy. The placement of the eye cameras for the OET and the placement of the electrodes for the EOG must be chosen consciously, the sampling rate must be minimal 200 Hz, and software and hardware must be robust to resist any factors influencing eye tracking. Conclusion Monitoring physiological and psychological readiness of soldiers, as well as other civil professionals that face higher risks when their attention is impaired or reduced, is necessary. However, improvements to eye-tracking devices’ hardware, calibration method, sampling rate, and algorithm are needed in order to accurately monitor fatigue levels in the field.

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Section: Eye-tracking Systems’ Feasibility In the ...mentioning

confidence: 99%

Detecting Soldiers’ Fatigue Using Eye-Tracking Glasses: Practical Field Applications and Research Opportunities

2021

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“…Bin Suhaimi et al [17] exploited a 24-point gazing data for which conversion is done simultaneously for presumption of an imaginary center (i.e., 25th point) on gaze coordinates with 24-point gazing data and transformation to 24-point gazing data is accomplished. To validate the results, conventional method is contrasted with the proposed method.…”

Section: Related Workmentioning

confidence: 99%

Accurate and Efficient Differentiation Between Normal and Epileptic Seizure of Eyes Using 13 Layer Convolution Neural Network

Brahmaiah¹,

Yarlagadda²,

Prasad³

2021

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Epileptic seizure is one which affects the normal brain activities of human being and considered to be a risky disease. The eye ball movement signals pattern plays a significant role in determining the epileptic seizure in precise manner. In addition to it, EOG signals has its influence in detecting epileptic seizure through assessment of eye ball movement signals precisely. Detecting Epilepsy using genetical based Convolutional Neural Network plays a major role in the previous research works. Conversely, the existence of background noise on eye ball signals may impact on the outcome failure. Noise aware Epileptic Seizure Detection using Thirteen Layer Convolution Neural Network (NESD-TLCNN) is adopted in this research to mitigate this issue and thereby ensuring the prediction rate more precisely. Furthermore, Hybrid Dynamic Time Wrapping based Hidden Markov Model (HDWT-HMM) is greatly utilized for primary background noise detection and removal by estimating the noise depending on distance metric. Once after the completion of noise estimation, perfect detection of epileptic seizure is accomplished using feature extraction. The peculiar features involved are saccade, fixation and blink features. Subsequently, Particle swarm optimization (PSO) technique is also involved in this research for optimal feature selection. Thirteen Layer Convolution Neural Network (TLCNN) is applied at last for learning and differentiation of epileptic seizure from the normal eyes. This research is being carried out in MATLAB platform which also reveals that the anticipated methodology produces improved outcomes when contrasted with the existing research work.

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“…The ON state was returned if the signal was above the baseline threshold of the envelope signal whereas, an OFF state resulted whenever the rectified signal was below the baseline threshold. The choice of the threshold was advantageous in aiding the removal of any residual noise of the envelope signal [38]. The muscle state is defined as in (3):…”

Section: Emg Analysismentioning

confidence: 99%

Mapping Three Electromyography Signals Generated by Human Elbow and Shoulder Movements to Two Degree of Freedom Upper-Limb Robot Control

et al. 2020

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This article sought to address issues related to human-robot cooperation tasks focusing especially on robotic operation using bio-signals. In particular, we propose to develop a control scheme for a robot arm based on electromyography (EMG) signal that allows a cooperative task between humans and robots that would enable teleoperations. A basic framework for achieving the task and conducting EMG signals analysis of the motion of upper limb muscles for mapping the hand motion is presented. The objective of this work is to investigate the application of a wearable EMG device to control a robot arm in real-time. Three EMG sensors are attached to the brachioradialis, biceps brachii, and anterior deltoid muscles as targeted muscles. Three motions were conducted by moving the arm about the elbow joint, shoulder joint, and a combination of the two joints giving a two degree of freedom. Five subjects were used for the experiments. The results indicated that the performance of the system had an overall accuracy varying from 50% to 100% for the three motions for all subjects. This study has further shown that upper-limb motion discrimination can be used to control the robotic manipulator arm with its simplicity and low computational cost.

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24-Gaze-Point Calibration Method for Improving the Precision of AC-EOG Gaze Estimation

Cited by 10 publications

References 20 publications

Detecting Soldiers’ Fatigue Using Eye-Tracking Glasses: Practical Field Applications and Research Opportunities

Detecting Soldiers’ Fatigue Using Eye-Tracking Glasses: Practical Field Applications and Research Opportunities

Accurate and Efficient Differentiation Between Normal and Epileptic Seizure of Eyes Using 13 Layer Convolution Neural Network

Mapping Three Electromyography Signals Generated by Human Elbow and Shoulder Movements to Two Degree of Freedom Upper-Limb Robot Control

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