Application and Visualization Challenges of Machine Learning in Smart Kitchen Adaptation for the Elderly

Abstract

This paper aims to systematically explore the integrated application of artificial intelligence and data visualization technologies in aging-in-place modifications for smart kitchens. The research constructs a comprehensive system framework capable of integrating multi-source perception data. Leveraging machine learning algorithms, this framework continuously learns and dynamically models the daily behavioral habits of elderly users alongside kitchen environmental parameters. This enables precise identification and early warning of safety hazards such as gas leaks and fall accidents. Building upon this foundation, the system translates multidimensional analytical results into intuitive visual interfaces. It delivers real-time assistance through intelligent alerts and interaction methods tailored to the cognitive characteristics of the elderly. This data-driven technical approach comprehensively enhances the smart kitchen's safety, operational ease, and human-machine interaction friendliness, providing effective technological support for enabling older adults to achieve more autonomous and secure home living.