Incremental Update Algorithm of Athlete Physical Training Information Under Dynamic Iterative Sampling

Abstract

An incremental update algorithm of athlete physical training information based on dynamic iterative sampling is proposed to address the problems of lack of real-time and low computational efficiency in the process of athlete physical training information analysis. The dynamic iterative sampling technique is combined to collect large-scale athlete fitness data, obtain athlete fitness training information based on the incremental update framework, map the existing athlete fitness training data input values into the high-dimensional feature space of the informational network, and combine with the incremental learning algorithm to perform fast updates to better understand the athletes’ fitness status. The experimental results show that the root mean square error, the average relative error and the correlation coefficient of the samples after the application of this algorithm are better. It reflects the athletes’ physical training situation more accurately and has certain application value.