Exercise Intensity Assessment Algorithms Based On Knowledge Graph

Exercise intensity is an important index in physical exercise and sports training,which refers to the amount of exercise in a certain time or unit.Studies have shown that moderate exercise intensity can effectively improve physical function,reduce the incidence of a variety of diseases,and play a positive role in preventing mental illness and relieving emotional problems.But the too large exercise intensity,which is more than the body can handle,will reduce the effect of exercise,the body function decline,and even threatens lives.How to grasp and control their exercise intensity is one of the practical problems that many bodybuilders need to solve.Traditional exercise intensity evaluation techniques are dependent on experience,equipment and other defects.Knowledge graph can not only use nodes and relations to associate different knowledge,but also has certain reasoning function.Therefore,this paper proposes an exercise intensity evaluation method based on knowledge graph,and uses knowledge reasoning to build a system.The system evaluates users based on different forms and exercise effects,making the evaluation results more precise and personalized,so as to better help users choose their own suitable exercise intensity.This thesis conducts research on exercise intensity assessment based on knowledge graph technology,which mainly includes the following contents:(1)Construction of exercise intensity knowledge graph.There are many indexes of exercise intensity evaluation,and the evaluation can be directly affected by many factors.In this paper,the concept layer of exercise intensity knowledge graph is constructed manually.Then,by introducing structured knowledge such as assessment grade and exercise prescription,and unstructured exercise intensity knowledge extracted by BERT-CRF,the exercise intensity knowledge graph was established in Neo4 j database.(2)Design of knowledge reasoning model.Based on Rec GNNs knowledge reasoning,weight rules are constructed according to expert advice and actual test situation.The model can match and find the appropriate exercise intensity and program to achieve personalized and accurate assessment of exercise intensity for different age groups.(3)Construction of exercise intensity evaluation system.Based on the knowledge graph and reasoning model,the exercise intensity evaluation system was constructed,and the comparison experiment was carried out among different populations.The experimental results show that the exercise intensity evaluation system based on knowledge graph is stable and reliable,and the accuracy of personalized evaluation can reach 85.7%.By comparing with the traditional exercise intensity detection technology,the personalization and accuracy of the exercise intensity evaluation method based on knowledge graph are illustrated.