Application of Improved VMD-LSTM Model in Sports Artificial Intelligence

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 6
• Main Authors: Zhang, Tiancong, Fu, Caihua
• Format: Journal Article
• Language: English
• Published: New York Hindawi 14.07.2022; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Algorithms; Artificial intelligence; Back propagation networks; Bandwidths; Computer science; Deep learning; Education; EKG; Electrocardiography; Iterative methods; Knowledge management; Kurtosis; Machine learning; Monitoring; Neural networks; Physical education; Recognition; Signal processing; Signal to noise ratio; Sporting goods; Sports; Sports injuries; Training; Wearable technology
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2022/3410153

In recent years, with the rapid development of a new generation of artificial intelligence technology, how to deeply apply artificial intelligence technology to physical education and break through the limitations of time-space scenarios and knowledge transfer methods in traditional models has become a key issue in intelligent physical education in the era of artificial intelligence. In order to realize the online monitoring of wearable devices with artificial intelligence in sports and overcome the problem of low recognition accuracy of electrocardiogram, blood oxygen, and respiratory signals in many cases, this paper proposes a combination of variational modal decomposition based on the maximum envelope kurtosis method. Long-short-term neural network (VMD-LSTM) monitoring method for wearable sports equipment. Through experimental analysis and verification, the current signal of the VMD model shows a trend of fluctuating from large to stable and then to large with motion, while the training accuracy of LSTM after the 150th iteration is 94.09%, which shows that the coupling model VMD LSTM can better predict the direction of sports artificial intelligence. In addition, although the training time of the BP neural network is shorter than that of the LSTM model, there is a large gap between the recognition effect and the LSTM, and there are also large differences between different neural network structures. This shows that the VMD-LSTM model has broad application prospects in such models.