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Hyeokjin CHOI; Seung Hyun CHA

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Lifestyle diversification and technological advancement have expanded the scope of confined space design beyond traditional small-scale store and housing to include autonomous vehicles and spacecraft. In confined spaces, body-scale movements directly impact spatial efficiency and occupant experience. As dimensions decrease, minor ergonomic factors become critical, yet designers lack empirical behavioral data, relying on intuition rather than actual usage patterns. Recent developments in XR devices present a viable solution. These devices capture first-person visual data, calculate movement patterns, and enable pose estimation, generating valuable design inputs. Despite this potential, systematic approaches to integrate egocentric vision data into design processes remain underdeveloped. To address this gap, we propose a design strategy for utilizing egocentric vision data in confined space design. Our strategy consists of three stages: (1) Capturing Egocentric Data establishes protocols for collecting relevant behavioral information from XR devices; (2) Mapping Design Parameter converts raw data into actionable design parameters through processing; and (3) Refining Design Outcomes implements iterative validation to optimize spatial configurations. We detail our strategy through a case study of a small kitchen, demonstrating how the approach adapts to different task types by analyzing two distinct scenarios: (i) a high-frequency simple task and (ii) a low-frequency complex task. This research establishes a systematic strategy for leveraging egocentric vision data, offering designers a guideline to integrate emerging technologies into evidence-based spatial design, while ultimately underscoring that as task complexity increases, the importance of a designer's expert judgment grows in parallel with data utilization.

Keywords:

Egocentric Vision, Confined Space Design, Human Behavior, Extended Reality