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This research investigates fatigue’s impact on arm gestures within augmented reality environments. Through the analysis of the gathered data, our goal is to develop a comprehensive understanding of the constraints and unique characteristics affecting the performance of arm gestures when individuals are fatigued. Based on our findings, prolonged engagement in full-arm movement gestures under the influence of fatigue resulted in a decline in muscle strength within upper body segments. Thus, this decline led to a notable reduction in the accuracy of gesture detection in the AR environment, dropping from an initial 97.7% to 75.9%. We also found that changes in torso movements can have a ripple effect on the upper and forearm regions. This valuable knowledge will enable us to enhance our gesture detection algorithms, thereby enhancing their precision and accuracy, even in fatigue-related situations. 

In this digital learning era, Augmented Reality (AR) has become a significant driver of innovative user experience. However, the ergonomic implications of AR, particularly regarding the postural fatigue dynamics, have not been comprehensively addressed. This study investigates the correlation between prolonged AR engagement and the onset of postural fatigue, characterized by a backward shift in the center of mass (COM). Employing motion capture technology alongside cognitive load assessment tools such as the NASA Task Load Index and HoloLens eye-tracking, we seek to quantify the relationship between user posture, engagement duration, and perceived workload. We hypothesize that an observable rearward displacement of COM signifies escalating fatigue levels. The methodology integrates ergonomic analysis, biomechanics, and predictive modeling. Preliminary findings indicate a decline in postural stability with increased AR exposure, reinforcing the need for ergonomics interventions. This study underscores the necessity of ergonomic consideration in the design and use of AR systems to safeguard user well-being in educational settings. 

In the digital learning landscape, Augmented Reality (AR) is revolutionizing instructional methodologies. This study shifts focus to explore the impact of AR-based lectures on pupil dilation as a biomarker of mental demand. By analyzing pupil dilation with cognitive load assessment tools like the NASA Task Load Index, we aim to understand the cognitive implications of prolonged exposure to AR in educational settings. We hypothesize that variations in pupil size can be indicative of cognitive load, correlating with the mental demands imposed by AR lectures. Preliminary findings suggest a significant relationship between increased pupil dilation and heightened mental workload during AR engagements. This study highlights a new way to measure cognitive workload in AR environments using pupil dilation data. 

This study examines the ergonomic impact of augmented reality (AR) technologies in educational contexts, with a focus on understanding how prolonged AR engagement affects postural dynamics and physical demands on users. By analyzing slouching scores alongside NASA Task Load Index (TLX) Physical Demand (PD) values, we assess the physical strain experienced by participants during the initial modules of an AR-based lecture series. Our findings demonstrate a notable decline in slouching scores as participants progress through the lecture modules, indicating increased postural deviations. To quantify these effects, we developed a regression model that effectively predicts the physical demands imposed by various AR modules, based on the observed slouching scores. 

In this study, we explore the impact of incorporating a virtual instructor with realistic lip-syncing in an augmented reality (AR) learning environment. The study is particularly focused on understanding if this enhancement can reduce students’ mental workload and improve system usability and performance in AR learning. The research stems from previous feedback indicating that a virtual instructor without facial movements was perceived as “creepy” and “distracting.” The updated virtual instructor includes facial animations, such as blinking and synchronized lip movements, especially during lecture explanations. The study aims to determine if there are significant changes in mental workload and usability differences between the AR systems with and without the enhanced virtual instructor. The study found significant differences in the usability scores in some questions. However, there was no significant difference in the mental workload between them. 

Augmented Reality revolutionises education by enhancing learning with interactive, immersive experiences. However, the impact of long-term AR use, particularly in terms of physical demand, within educational environments remains poorly understood. This study investigates the relationship between AR engagement and physical demand, utilising motion capture technology, NASA Task Load Index, and HoloLens eye-tracking to quantify user posture, engagement, and perceived workload. We hypothesise that prolonged AR interaction results in a change in slouching scores, indicating increased fatigue. The results show a strong correlation between the slouching score and the NASA-TLX physical demand score. Our study lays the groundwork for incorporating predictive modelling to develop proactive physical demand measures. 

This research aims to explore the prediction of student learning outcomes in Augmented Reality (AR) educational settings, focusing on engineering education, by analyzing pupil dilation and problem-solving time as key indicators. In this research, we have created an innovative AR learning platform through the incorporation of eye-tracking technology into the Microsoft HoloLens 2. This enhanced learning platform enables the collection of data on pupil dilation and problem-solving duration as students engage in AR-based learning activities. In this study, we hypothesize that pupil dilation and problem-solving time could be significant predictors of student performance in the AR learning environment. The results of our study suggest that problem-solving time may be a critical factor in predicting student learning success for materials involving procedural knowledge at low difficulty levels. Additionally, both pupil dilation and problem-solving time are predictive indicators of student learning outcomes when dealing with predominantly procedural knowledge at high difficulty levels.