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Measuring physical demand in Augmented Reality learning environments.

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...

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Published in:Ergonomics Vol. 68; no. 12; pp. 1998 - 2011
Main Authors: Pulipati, Varun, Kim, Jung Hyup, Wang, Fang, Oprean, Danielle, Seo, Kangwon
Format: clinical trial equations & formulas pictorial research tables/charts Journal Article
Published: Taylor & Francis Ltd Dec2025
Online Access:View this record in EBSCOhost
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      dt: Dec2025
      vid: 68
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      pub: Taylor & Francis Ltd
      place: Philadelphia, Pennsylvania
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        10.1080/00140139.2024.2447870
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        atl: Measuring physical demand in Augmented Reality learning environments.
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        au:
          Pulipati, Varun
          Kim, Jung Hyup
          Wang, Fang
          Oprean, Danielle
          Seo, Kangwon
        affil: Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
      sug:
        subj:
          Augmented Reality
          Learning Environment
          Task Performance and Analysis
          Educational Technology
          Motion Analysis Systems Utilization
          Funding Source
          Human
          Eye Movements
          Posture
          Fatigue
          Ergonomics
          Workload
          Regression
          Prediction Models
          Linear Regression
          Questionnaires
          Usability Study
          Clinical Trials
          Scales
          Correlation Coefficient
      ab: 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. Practitioner Summary: Measuring physical demand during AR-based learning is possible using the slouching score. This metric enables dynamic assessment of user physical demand in AR environments, paving the way for improved AR system design to minimise physical fatigue during tasks or learning, enhancing overall user comfort and performance.
      pubtype: Academic Journal
      doctype:
        clinical trial
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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