Application of machine learning based on optical sensor detection in real-time data simulation of aerobics competitions

In the process of aerobics competition, it is necessary to evaluate the training and performance of athletes and coaches with real-time data. However, traditional data recording methods have some limitations, such as relying on manual recording or being limited by the number and location of sensors. The research aims to use light sensors and machine learning techniques to develop an application that can detect, record and simulate aerobics competition data in real time. The movement trajectory and posture information of athletes are captured by light sensors, and the data is analyzed and simulated with the help of machine learning algorithms to provide more accurate and comprehensive competition data. The light sensor can accurately detect the posture and movement of the human body, and transmit the data to the computer through the data transmission module. The sensor data is processed and analyzed using machine learning algorithms, which are trained to recognize specific movements and postures and compare them to preset criteria. The results show that athletes and coaches can obtain real-time competition data through the application, and analyze and feedback to improve the accuracy of training effect and performance evaluation.