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Updated: Jul 1, 2026
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Virtual Reality Experiments with Physiological Measures
Published on: August 29, 2018
Graph-based analysis of work-from-home layouts and affective responses in virtual reality
1Department of Architecture, Joongbu University, Goyang, Republic of Korea.
Scientific Reports
|June 29, 2026
Summary
This study uses graph-based learning in virtual reality to model how work-from-home furniture layouts affect user feelings. It found that spatial relationships, like desk placement, significantly influence user experience and workspace perception.
Area of Science:
- Human-Computer Interaction
- Computational Social Science
- Environmental Psychology
Background:
- Understanding the link between physical workspace design and user well-being is crucial for effective work-from-home (WFH) environments.
- Traditional methods struggle to capture complex spatial relationships in interior design.
- Virtual reality (VR) offers a controlled environment to study user responses to various layouts.
Purpose of the Study:
- To develop and evaluate a graph-based learning framework for modeling associations between WFH furniture layouts and user affective responses.
- To investigate the impact of spatial relationships between furniture and architectural elements on user experience in VR.
- To demonstrate the methodological advantages of graph representations for analyzing interior space-user interaction.
Main Methods:
- A graph-based learning framework using GraphSAGE with attention pooling was developed to predict user affective responses.
- Eighty-five work-from-home (WFH) furniture layouts were created and evaluated by 43 participants in a virtual reality (VR) environment.
- Layouts were represented as spatial graphs, with furniture and architectural elements as nodes and proximity as edges, and rated on six outcomes.
Main Results:
- The graph-based model achieved a mean absolute error of 0.684 and a variance-weighted R² of approximately 0.659 in predicting user responses.
- The graph model outperformed non-graph baseline models in prediction accuracy for this dataset.
- GNNExplainer identified key influential subgraphs, particularly desk-related spatial configurations (e.g., desk-window, desk-door).
Conclusions:
- Graph-based representation provides a valuable methodological approach for evaluating the association between object-level spatial relationships in WFH layouts and user experience.
- The study highlights the importance of specific furniture arrangements and their proximity to architectural features in shaping user perception and satisfaction.
- This framework offers a novel way to analyze and optimize interior spatial designs for improved user affective responses.
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