Personalized Motion Scheme Generation System Design for Motion Software Based on Cloud Computing

The increase of national attention has also promoted the growth of the scale of sports health industry. However, for ordinary people who lack professional knowledge, intuitive data cannot make correct sports planning. Therefore, aiming at the problem that it is difficult for ordinary people to make correct exercise plan according to intuitive data, a personalized exercise plan generation system based on cloud computing is proposed. By analyzing the user's movement and physical data, the system uses cloud computing resources and machine learning algorithms to provide customized exercise recommendations for users. The key innovation of the research is the combination of improved random forest algorithm and reinforcement learning, while improving the performance of the algorithm on unbalanced sample sets. The results indicated that the accuracy of the improved random forest was 0.985 higher than that of the precision weighted random forest. The research algorithm was 9.04% higher on average than the original random forest algorithm and 2.71% higher than the accuracy weighted random forest algorithm. In terms of the accuracy of personalized motion scheme generation of motion software, the improved algorithm reached 95.05% at most, and its recall rate reached 83.46% at most. Compared with the existing sports software solutions, the research system can generate personalized sports programs more accurately, promote the development of the sports health industry and improve the national physical health level. The system can provide users with personalized sports suggestions, and utilize the powerful computing power of cloud computing to realize real-time processing and analysis of large-scale user data, providing users with timely sports feedback and suggestions.