Constructing an exercise knowledge graph can provide intuitive and convenient guidance for scienti-fic exercise,significantly contributing to the development of the fitness course and the promotion of public health.However,there are currently few studies on how to comprehensively acquire multi-modal knowledge of scientific exercises and systematically integrate this knowledge to form a multi-modal knowledge graph.This task faces numerous challenges.To address these issues,we explore the construction of a multi-modal scientific fitness exercise knowledge graph using natural language processing,deep learning,and visual recognition techn-ologies in a human-machine collaborative manner.The specific steps include:1)data selection and processing to ensure the reliability and comprehensiveness of the foundational data for the knowledge graph;2)designing a multimodal knowledge system to integrate various forms of information,such as text,images,and videos;3)empl-oying key technologies to acquire knowledge,particularly converting exercise videos into fine-grained textual descriptions;4)building a visualization platform to make the knowledge graph more intuitive and user-friendly.By systematically relating and integrating multi-modal fitness knowledge,we have successfully constructed a fitness exercise knowledge graph rich in information.This graph not only effectively converts exercise videos into detailed textual descriptions but also intuitively displays the information through a visualization platform,providing users with easy-to-understand and user-friendly scientific exercise guidance.The construction of the multi-modal scientific exercise knowledge graph provides an important reference for the construction and appli-cation of knowledge graphs in the fitness field.This indicates that acquiring and integrating multi-modal knowledge is an essential approach to achieving scientific fitness guidance,which can effectively promote the development of the fitness course and enhance people's health.
维普
万方数据