Coordination Training and Testing of Upper and Lower Limbs in Aerobics Under Neural Networks

This paper mainly studies the application of neural network models in the training and testing of upper and lower limb coordination in aerobics. By analyzing deep learning algorithms, we can accurately assess and predict the coordination ability of aerobics athletes, thereby improving their competitiveness. We introduce the application of neural network algorithms in sports training and testing, especially for testing upper and lower limb coordination in aerobics. We use deep learning algorithms, such as convolutional neural networks (CNN), to evaluate and predict the coordination of athletes’ upper and lower limbs. Our research results show that neural network algorithms have broad application prospects in the training and testing of upper and lower limb coordination in aerobics.