Utilizing Big Data and Machine Learning to Improve Aerobic Exercise Data Collection and Analysis

Traditional data collection and analysis methods are manually recorded and analyzed, with low efficiency and poor accuracy. Additionally, due to limitations in the number of athletes and equipment, only a small number of data can be recorded, which cannot fully reflect the effectiveness of aerobic exercise. This poses significant limitations for the development of scientific fitness programs and the evaluation of their effectiveness. This article aims to improve the accuracy, real-time performance, and personalization of exercise data, in order to better guide and improve the exercise effectiveness and health status of users. This article utilizes BD (Big Data) and ML (Machine Learning) techniques to improve the collection and analysis of aerobic exercise data. This can improve the efficiency and accuracy of data collection and analysis, more comprehensively reflect the effects of aerobic exercise, and provide stronger support for scientific research and practice in the field of health. The accuracy rates of collecting athlete data for the control group and experimental group were 72.41% and 85.56%, respectively. The accuracy rate of collecting data for the experimental group was higher. The results of this article can optimize personalized exercise recommendations and predict health risks, improve exercise effectiveness and health management level.