Fatigue sensation of Eye Gaze Tracking System users

Suzuki, Yasuhiro; Yamamoto, Soh; Kobayashi, Daiji

doi:10.1201/b17990-49

Cited by 1 publication

(2 citation statements)

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“…This implies that Banister et al's [38] model is possibly suitable for describing the performance in the short term, as the DHO model deals well the performance in the long term. Some research suggests that users begin to sense the fatigue after 13 min of using a gazetracking system [47]. However, to prove this assumption on the performance models, more experiments are required.…”

Section: Resultsmentioning

confidence: 99%

“…Sommer et al [46] compare the accuracy of different fatigue-monitoring technologies (FMT) for detecting fatigue in driving simulations and find that FMT devices perform acceptably at low temporal resolution (>20 min) but have large errors when estimating fatigue at high temporal resolution. Finally, Suzuki et al [47] propose a model for detecting mental fatigue in natural viewing situations and an automated feature selection method to make the model robust to the target's age, showing that their model improves detection accuracy compared to previous studies. Overall, these papers suggest that eye-tracking technology is a promising tool for detecting the effects of fatigue and mental fatigue in various contexts.…”

Section: Overview Of Related Workmentioning

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: Resultsmentioning

confidence: 99%

Section: Overview Of Related Workmentioning

confidence: 99%