Integration of Cross-Computer Science and Architectural Design for the Elderly: AI for Smart Home

This study integrates the fields of computer science and architectural design to address the needs of the elderly population by incorporating artificial intelligence (AI) into smart home environments. Smart home technology, characterized by sensor networks and automation capabilities, has the potential to improve the quality of life and safety of the elderly. The use of AI algorithms, especially in action recognition from video images, offers new possibilities for personalized assistance and security in smart homes. Our methodology adopts a two-step approach: skeleton key point identification followed by action recognition using a novel convolutional neural network. Through a thorough review of smart home technologies, recent advances in automation, user-centric adoption approaches, and security and privacy measures are highlighted. Our proposed action recognition model integrates video sensors and AI analytics to provide voice feedback and Zigbee gateway design to enhance user interaction and connectivity. Evaluation through system functional assessment and numerical simulation demonstrates the effectiveness and stability of the proposed method, which achieves competitive performance in action recognition accuracy.