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Measuring physical demand in Augmented Reality learning environments
Varun Pulipati1, Jung Hyup Kim2, Fang Wang3
1Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.
Ergonomics
|November 17, 2025
Summary
Long-term use of Augmented Reality (AR) in education increases physical fatigue, as shown by a strong correlation between slouching posture and perceived workload. This research highlights the need for proactive measures to mitigate physical strain during extended AR engagement.
Area of Science:
- Educational Technology
- Human-Computer Interaction
- Ergonomics
Background:
- Augmented Reality (AR) offers immersive educational experiences.
- The physical demands of prolonged AR use in learning environments are not well understood.
- Investigating AR's impact on user physical well-being is crucial for sustainable adoption.
Purpose of the Study:
- To examine the relationship between AR engagement and physical demand in educational settings.
- To quantify changes in user posture and perceived workload during extended AR use.
- To establish a foundation for developing predictive models for physical demand in AR.
Main Methods:
- Utilized motion capture technology to track user posture.
- Employed the NASA Task Load Index (NASA-TLX) to assess perceived workload.
- Integrated HoloLens eye-tracking to monitor user engagement and interaction.
- Quantified slouching scores as an indicator of physical fatigue.
Main Results:
- A significant correlation was found between slouching scores and the physical demand component of the NASA-TLX.
- Prolonged AR interaction was associated with increased slouching, indicating heightened physical fatigue.
- Objective and subjective measures consistently indicated physical strain during AR use.
Conclusions:
- Extended AR use in education leads to measurable physical fatigue.
- Posture changes, specifically increased slouching, are reliable indicators of AR-induced physical demand.
- Findings support the development of ergonomic guidelines and predictive tools for AR educational applications.

