ANALYSIS OF BIOLO ARTIFICIAL NEURAL NETWORK IN PREDICTION OF AEROBIC EXERCISE INDEX BASED ON ALGORITHM

• Autor(a) principal: Ru,Lei
• Data de Publicação: 2021
• Outros Autores: Zhang,Bin, Duan,Jing, Ru,Guo
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista brasileira de medicina do esporte (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1517-86922021000400367
• topic: Biological images; Index; Image recognition; Neural networks, computer

ABSTRACT Objective: To study the relationship between aerobic activity and cardiac autonomic nerve activity by artificial neural network algorithm and biological image fusion; because of the artificial neural network model (ANN) problems, biological image processing technology is introduced based on ANN. Methods: An Ann under biological image intelligence algorithm is proposed, a classifier suitable for electrocardiograph (ECG) screening is designed, and an ECG signal screening system is successfully established. Moreover, the data set of normal recovered ECG signals of the subjects during the experimental period is constructed, and a classifier is used to extract the characteristic data of a normal ECG signal during the experimental period. Results: The changes in resting heart rate and other physical health indicators are analyzed by combining resting physiological indicators, namely heart rate, body weight, body mass index and body fat rate. The results show that the self-designed classifier can efficiently process the ECG images, and long-term regular activities can improve the physical conditions of most people. Most subjects’ body weight and body fat rate decrease with the extension of experiment time, and the resting heart rate decreases relatively. Conclusions: Certain indicators can be used to predict a person's dynamic physical health, which indicates that the experimental research of index prediction in this research has a good effect, which not only extends the application of artificial neural network but also lays a foundation for the research and implementation of ECG intelligent testing wearable devices. Level of evidence II; Therapeutic studies - investigation of treatment results.