Research And Design Of Personalized Exercise Prescription Recommendation System

In recent years,the national economy has developed rapidly and people’s living standards have been continuously improved.While paying attention to the quality of life,their own health issues have also been paid more and more attention.The public’s concept of fitness has also changed.People are no longer satisfied with fitness through medical equipment or blind fitness plans,but seek more professional and planned fitness methods,so as to avoid blind fitness.For example,obesity is an increasingly serious public health problem that has developed into an epidemic.Obesity is an important risk factor for diseases such as diabetes,high blood pressure,coronary heart disease,and even causes children to face psychological and social problems.In addition to diet control,reasonable exercise has a significant effect on the prevention and treatment of this chronic disease.The concept of "exercise is a good medicine" has deeply affected Chinese people.Therefore,how to provide users with a practical exercise prescription program has become a point of concern for the public.To solve this problem,personalized recommendation technology is an effective solution.It can help users choose the most suitable one among a large number of exercise prescriptio ns and recommend them to users,so that exercise prescriptions are more in line with user needs.In order to better combine recommendation technology with exercise prescription and recommend more satisfactory exercise prescriptions to users,this article studies the content of recommendation technology and analyzes the existing personalized exercise prescription recommendation methods and the comparison of various recommenda t io n methods.,Proposed a cluster-based exercise prescription method,and then combined with the user’s hobbies to provide users with more personalized and accurate exercise prescriptions.The main work of this paper is as follows:(1)This thesis proposes an exercise prescription recommendation method based on K-means clustering.Through user feature information clustering,features in the same category are similar,the new user is compared with the center point of each cluster to obtain the cluster where the user is located,and then the similarity comparison is performed to obtain the user with the highest feature similarity,and recommend their exercise prescription,that is,the preliminary personalized exercise prescription.(2)This thesis proposes a recommendation method based on user preferences.By analyzing the characteristic information of new users,the user’s preference characteristics are obtained.Research the sports items that users may be interested in through preference characteristics,then make adjustments to the prelimi nar y personalized exercise prescription,and recommend the final personalized exercise prescription.(4)Design an APP application and apply the algorithm to the system to realize the main functions of the system.