Data Estimation of Injury-Prone Sports by 3D Image Joint Probability Distribution

Abstract

The objective of the paper is to study and analyze sports injuries from multiple perspectives. The reduction of damage risk factors of young athletes in sports would reduce or avoid the occurrence of sports injuries. The obtained point cloud data is preprocessed by three-dimensional (3D) image technology for the registration of the cloud data to be registered. The parallel Bayesian network structure was established to realize the parallelization of the probability distribution estimation algorithm. The research results have shown that the parallel probability distribution estimation algorithm would provide a great degree of efficiency improvement in terms of solving continuous function optimization and real-time optimization problems, as well as the problems of improving the efficiency of the algorithm to some extent. It could be inferred that the combination of improved rasterized nearest point search method has shortened the search time of the nearest point, thereby improving the efficiency of the algorithm. The correctness and rationality of the algorithm were verified from both theoretical and practical perspectives.