Priority Analysis of Wandering Alarm Device Functions for Elderly Users Based on the KANO-QFD Model with Machine Learning Algorithm

Nowadays, many countries are experiencing an accelerated aging process. However, there are a huge amount with frequent incidents of elderly individuals getting lost. Existing anti-wandering tools face issues such as homogeneous functionalities and inadequate adaptation to user needs. To address the disconnect between technology-driven innovations and user experiences, this study introduces an integrated KANO-QFD model to construct a methodological framework for translating elderly user needs into engineering characteristics and enhancing real-time response capabilities through artificial intelligence-driven innovations such as predictive algorithms. By categorizing demands through the KANO model (must-be, one-dimensional, and attractive qualities) and integrating Quality Function Deployment (QFD) for demand weight mapping and technical parameter optimization, the research overcomes limitations of subjective demand surveys and technological innovation constraints. Through large-scale questionnaire surveys (506 valid samples) and quantitative analysis, the study identifies core, expected, and attractive demands for anti-wandering tools while eliminating reverse demands. This research provides a scientific decision-making framework for optimizing tool functionalities, shifting products from "technology stacking" to "precision services," and offers theoretical paradigms and practical references for developing smart devices in an aging society.