Coordination Training and Testing of Upper and Lower Limbs in Aerobics under Neural Network Algorithm
Jianli Wang, Ruichun Gu
Digital Signal Processing and Artificial Intelligence for Automatic Learning, Vol. 3, No. 4, 2024.
DOI: https://doi.org/10.6025/dspaial/2024/3/4/121-127
Link: https://www.dline.info/dspai/fulltext/v3n4/dspaiv3n4_1.pdf
Abstract:
This paper mainly studies the application of computer neural network algorithms 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 computer 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 athletesâ€™ upper and lower limbs. Our research results
show that computer neural network algorithms have broad application prospects in
the training and testing upper and lower limb coordination in aerobics.