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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. 

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. 

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.