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Motion capture for personalized living experience enhancement
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School of Arts, Design and Architecture |
Master's thesis
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en
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71
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Abstract
This research explores how motion capture and virtual space anaIysis enhance personaIized design in residentiaI environments. By integrating 3D scanning, motion capture, and interactive virtual environments, the research uses a specific existing room as a research case to develop a methodology that can visualize and analyze resident’s behaviors in this space, and propose a design strategy for living quality enhancement.
Behavioral analysis is always one of the criticaI methods for design because design quality is reflected in user behavior directly. However, existing analyses rely on experience-based plan analysis or wearable motion capture sensors, focusing on efficiency but miss deeper motivations behind those behaviors. Due to technological limitations and a lack of personalized data, these methods are rarely applied in residential contexts.
As demand for personalized living environments grows, these traditional behavioral analysis and generalized design conclusions are no longer sufficient to uncover subconscious behavioral patterns at home.This research develops and tests a set of 3D visualisation methods and workflow to reexamine the relationship between behavior and space, helping designers identify overlooked needs and enhance residential experiences.
This research is based on a case study of a senior housing unit, observed through the daily activities of a resident over a defined period. The research constructs a comprehensive analytical workflow which is presented as a proof-of-concept rather than a statistically generalisable study. Real-life scenarios are reconstructed through 3D scanning and AI tools, and using video-based motion capture to reconstruct motion animations. And in Unity, visualization techniques, including view cone analysis, senior comfort assessments, and spatial utilization mapping—interpret spatial issues and propose targeted design suggestions.
The core contribution of this research lies in proposing an integrated methodology that transcends traditional behavioral analysis. By translating recorded spatial interactions into visual and quantitative data, the approach provides a basis for precise, personalised design proposals. This method holds potential for broader applications, such as home renovation, specific commercial and exhibition layout optimization, and spatial enhancement in senior care facilities and public buildings.