Psychological Role of Design Elements on Human for Spatial Perception in Interior Design: Exploration of Interior Spaces and Machine-Learning Insights

Abstract Interior design impacts human well-being by optimizing spatial elements for functional and aesthetic spaces. This study aims to identify key elements in constrained interiors emphasizing the importance of understanding participants' psychological satisfaction within those spaces. A Delphi survey with 42 participants and model simulation was conducted to identify design elements and their effects on spatial perception (RII and Cronbach alpha provided reliability). Lighting conditions were estimated with field measurements: Luxmeter and illuminance simulation. The comparison between survey results, and field analysis is used to evaluate 3D-Design modifications. By comparing the before and after modification of 3D images, the model was improved depending on the satisfaction level of the participants. Additionally, three machine learning (ML) models were incorporated to predict participant satisfaction. The results show removing partition walls creates a seamless flow between the living area, enhancing illuminance up to 550%. Neutral colors (white and beige) brighten the space, built-in wardrobes and under-bed storage optimize space utilization (27%). Recessed lighting provides shadow-free illumination, while pendant lights add focal points above the dining table. The ML analysis demonstrated the potential for predicting participant satisfaction based on design elements, with the XGBoost model achieving up to 85% accuracy. These findings provide actionable insights that architects and other design experts can apply to improve living conditions, particularly in urban areas where space is constrained. Overall, the study highlights residents' emotional well-being and satisfaction in limited living spaces by employing a multidisciplinary approach that combines field measurements, machine learning, and advanced simulation techniques.