Artificial intelligence-driven personalized space design and implementation in the aging-friendly renovation of smart home

Against the backdrop of the global rapid transition to an aging society, how to utilize smart home technology to achieve efficient and personalized aging-friendly transformation has become a common focus in academic and practical fields. This study proposes a space generation and optimization framework integrated with Artificial Intelligence (AI), aiming to create a dynamically adaptive living environment for elderly users in a data-driven manner. First, the study deploys a multi-type sensor system in typical elderly households to collect multi-dimensional data, including behavior trajectories, activity intensity, and spatial usage heat of space usage. Subsequently, it uses Long Short-Term Memory (LSTM) network to model time-series behaviors, extract daily activity patterns, and establish a mapping relationship between behaviors and needs based on these patterns. On this basis, a Reinforcement Learning (RL) mechanism is introduced. By constructing a reward function centered on safety, convenience, and individual preferences, the spatial layout is continuously optimized through iteration. Furthermore, Conditional Generative Adversarial Network (cGAN) is used to generate spatial sketch designs, which significantly improves the efficiency of interaction and visualization. The study conducts empirical verification in three elderly household samples. Comparative experimental results show that: The coverage rate of activity areas increases to 71.6%, the spatial idle rate decreases to 12.9%, and the user satisfaction score rises from 2.9 to 4.7 (out of 5). Meanwhile, the behavior recognition accuracy of the LSTM model reaches 91.8%. The spatial layout adaptability optimized by RL increases by 22.7%. In addition, the user feedback mechanism effectively promotes continuous optimization, significantly enhancing the system’s personalized response capability. In general, this study achieves two main objectives. Firstly, it proposes an intelligent space generation and optimization method specifically designed for elderly users. Secondly, it addresses the deficiencies in existing research regarding dynamic adaptation and personalized design. This is accomplished through the synergy of behavior modeling and generative optimization. As a result, the study provides a technically practical path for the aging-friendly transformation of smart homes.