Visual Fatigue Estimation by Eye Tracker with Regression Analysis

Lin, Cheng‐Li; Chou, Li‐Wei; Chang, Kang-Ming; Wang, Jing-Fong; Chen, Sih-Huei; Hendradi, Rimuljo

doi:10.1155/2022/7642777

Cited by 13 publications

(10 citation statements)

References 29 publications

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“…Li et al [40] propose a four-phase framework that analyzes spatial and temporal gaze patterns to assess vigilance levels in traffic controllers. Bafna-Rührer et al [41] explore the feasibility of mental fatigue detection using smooth-pursuit movements in an eye-interactive task.…”

Section: Overview Of Related Workmentioning

confidence: 99%

“…The Fatigue Threshold (TF) [41,75] is a value that is calculated using an empirical formula. The formula depends on the average spatial accuracy of the eye tracker, denoted as θavg.…”

Section: Gaze Performance Metricsmentioning

confidence: 99%

“…Average spatial accuracy [41,77] is calculated during calibration by finding the mean gaze point, G i , for each calibration point, P i , and then calculating the average distance in degrees, θ i , between each calibration point and gaze point.…”

Section: Gaze Performance Metricsmentioning

confidence: 99%

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A Human-Adaptive Model for User Performance and Fatigue Evaluation during Gaze-Tracking Tasks

2023

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Eye gaze interfaces are an emerging technology that allows users to control graphical user interfaces (GUIs) simply by looking at them. However, using gaze-controlled GUIs can be a demanding task, resulting in high cognitive and physical load and fatigue. To address these challenges, we propose the concept and model of an adaptive human-assistive human–computer interface (HA-HCI) based on biofeedback. This model enables effective and sustainable use of computer GUIs controlled by physiological signals such as gaze data. The proposed model allows for analytical human performance monitoring and evaluation during human–computer interaction processes based on the damped harmonic oscillator (DHO) model. To test the validity of this model, the authors acquired gaze-tracking data from 12 healthy volunteers playing a gaze-controlled computer game and analyzed it using odd–even statistical analysis. The experimental findings show that the proposed model effectively describes and explains gaze-tracking performance dynamics, including subject variability in performance of GUI control tasks, long-term fatigue, and training effects, as well as short-term recovery of user performance during gaze-tracking-based control tasks. We also analyze the existing HCI and human performance models and develop an extension to the existing physiological models that allows for the development of adaptive user-performance-aware interfaces. The proposed HA-HCI model describes the interaction between a human and a physiological computing system (PCS) from the user performance perspective, incorporating a performance evaluation procedure that interacts with the standard UI components of the PCS and describes how the system should react to loss of productivity (performance). We further demonstrate the applicability of the HA-HCI model by designing an eye-controlled game. We also develop an analytical user performance model based on damped harmonic oscillation that is suitable for describing variability in performance of a PC game based on gaze tracking. The model’s validity is tested using odd–even analysis, which demonstrates strong positive correlation. Individual characteristics of users established by the damped oscillation model can be used for categorization of players under their playing skills and abilities. The experimental findings suggest that players can be categorized as learners, whose damping factor is negative, and fatiguers, whose damping factor is positive. We find a strong positive correlation between amplitude and damping factor, indicating that good starters usually have higher fatigue rates, but slow starters have less fatigue and may even improve their performance during play. The proposed HA-HCI model and analytical user performance models provide a framework for developing an adaptive human-oriented HCI that enables monitoring, analysis, and increased performance of users working with physiological-computing-based user interfaces. The proposed models have potential applications in improving the usability of future human-assistive gaze-controlled interface systems.

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Section: Overview Of Related Workmentioning

confidence: 99%

“…The Fatigue Threshold (TF) [41,75] is a value that is calculated using an empirical formula. The formula depends on the average spatial accuracy of the eye tracker, denoted as θavg.…”

Section: Gaze Performance Metricsmentioning

confidence: 99%

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A Human-Adaptive Model for User Performance and Fatigue Evaluation during Gaze-Tracking Tasks

2023

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“…One of the commonly used methods is to calculate the runoff data using statistical methods, and the other is to set up a prediction model according to the evolution of the predicted subject. We used statistical methods including linear regression and nonlinear regression, based on the observed data, to predict and analyze the runoff situations [32][33][34][35][36][37][38]. Speaking specifically, based on the monthly runoff data of the upper mountain areas of the Tarim River basin, from the Alar station, during the period of 1961-2010, and precipitation and temperature data in mountainous areas, the influence of the temporal-spatial component of regional precipitation and temperature on monthly runoff was analyzed by using least-squares methods and radial basis function (RBF) neural network.…”

Section: Regression Analysismentioning

confidence: 99%

The Climate Changes and the Simulation of the Runoff in the Last 50 years (1961–2010) in the Upper Tarim River Basin of Southern Xinjiang, China

Xiao

Ding

et al. 2022

Scientific Programming

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The upper Tarim River basin is supporting approximately 50 million people by melting the glaciers and snow, which are highly vulnerable and sensitive to climate change. Therefore, assessing the relative effects of climate change on the runoff of this region is essential not only for understanding the mechanism of hydrological response over the mountainous areas in Southern Xinjiang but also for local water resource management. This study quantitatively investigated the climate change in the mountainous area of the upper Tarim River basin, using the up-to-date “ground-truth” precipitation and temperature data, the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE, 1961–2010, 0.25°) data; analyzed the potential connections between runoff data, observed at Alar station, and the key climatological variables; and discussed the regression models on simulating the runoff based on precipitation and temperature data. The main findings of this study were as follows—(1) both annual precipitation and temperature generally increase at rates of 0.85 mm/year and 0.25 °C/10a, respectively, while the runoff data measured at the Alar station shows fluctuating decreasing trends. (2) There are significant spatial differences in the temporal trends of precipitation; for example, the larger increasing rates of precipitation occur in the Karakoram mountains, while the larger decreasing rates happen in the northwestern Kashgar county. (3) The decreasing trends of temperature mainly occur in Kashgar county and its surrounding areas in summer. (4) Seasonal correlations in precipitation and temperature trends are more significant than those on a monthly and annual scale. (5) The regression model in simulating the runoff in the upper Tarim River basin based on radial basis function (RBF) is better than that based on the least-squares method, with the predictive values based on RBF models significantly better (correlation coefficient, CC ∼ 0.85) than those by least-squares models (CC ∼ 0.75). These findings will provide valuable information to inform environmental scientists and planners on the climate change issues in the upper Tarim River basin of Southern Xinjiang, China, under a semiarid-arid climate.

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“…However, collecting information through self-reporting has several limitations and can be either consciously or unconsciously biased. In this context, more objective measures of visual discomfort and fatigue can be obtained from physiological signals, such as electro-oculogram (e.g., by detecting blink rates [7]), eye-tracking (e.g., by analyzing eye movements [8], [9]), and electroencephalography (EEG) (e.g., by exploiting changes in EEG power [10]). Indeed, physiological signals are less prone to subjective bias compared to self-assessed reports.…”

Section: Introductionmentioning

confidence: 99%

Behavioral, Peripheral, and Central Neural Correlates of Augmented Reality Guidance of Manual Tasks

Callara,

Rho,

Condino

et al. 2024

IEEE Trans. Human-Mach. Syst.

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The use of commercially available opticalsee-through (OST) head-mounted displays (HMDs) in their own peripersonal space leads the user to experience two perception conflicts that deteriorate their performance in precision manual tasks: the vergence-accommodation conflict (VAC) and the focus rivalry. In this work, we aim characterizing for the first time the psychophysiological response associated with user's incorrect focus cues during the execution of an augmented reality (AR)guided manual task with the Microsoft HoloLens OST-HMD. Methods: 21 subjects underwent to a "connecting-the-dots" experiment with and without the use of AR, and in both binocular and monocular conditions. For each condition, we quantified the changes in autonomic nervous system (ANS) activity of subjects by analyzing the electrodermal activity (EDA) and heart rate variability. Moreover, we analyzed the neural central correlates by means of power measures of brain activity and multivariate autoregressive measures of brain connectivity extracted from the electroencephalogram (EEG). Results: No statistically significant differences of ANS correlates were observed among tasks, although all EDA-related features varied between rest and task conditions. Conversely, significant differences among conditions were present in terms of EEG-power variations in the μ (8-13) Hz and β (13-30) Hz bands. In addition, significant changes in the causal interactions of a brain network involved in motor movement and eye-hand coordination comprising the precentral gyrus, the precuneus, and the fusiform gyrus were observed. Conclusion: The physiological plausibility of our results suggest promising future applicability to investigate more complex scenarios, such as ARguided surgery.

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Visual Fatigue Estimation by Eye Tracker with Regression Analysis

Cited by 13 publications

References 29 publications

A Human-Adaptive Model for User Performance and Fatigue Evaluation during Gaze-Tracking Tasks

A Human-Adaptive Model for User Performance and Fatigue Evaluation during Gaze-Tracking Tasks

The Climate Changes and the Simulation of the Runoff in the Last 50 years (1961–2010) in the Upper Tarim River Basin of Southern Xinjiang, China

Behavioral, Peripheral, and Central Neural Correlates of Augmented Reality Guidance of Manual Tasks

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