Biodata-Driven Knowledge Graph Recommendation System: Fusing Foot and Leg Characteristics for Personalised Shoe Recommendation

(1) This study aims to enhance the precision of ergonomic fitting in traditional shoe size selection by integrating literature and measured biometric data. (2) A correlation table between biometric features and shoe models was established, which was then embedded into a knowledge graph (KG) for visual, accurate recommendations. The experiment employed pressure sensors and depth cameras to collect biometric data from the foot and leg, evaluating the consistency of the system’s recommendations and user satisfaction. (3) The results indicate that the biometric-driven shoe recommendation system significantly outperforms traditional size-based systems in terms of stability and satisfaction. (4) The KG framework has notably improved ergonomic adaptability in the early prototype stage, offering a viable technological approach for intelligent shoe selection and holding significant potential for further optimization.