Abstract
ABSTRACT To enhance the efficacy of intermittent hypoxia training in sports, this study presents an intelligent training model that utilizes a graph neural network. The model incorporates the particle filter method to establish a real-time processing system for physiological signals generated during intermittent hypoxia training, enabling frequency tracking and network sorting. Additionally, an ARMA model is utilized to facilitate real-time carrier frequency estimation and time-hopping detection of physiological signals. An enhanced frequency tracking method is proposed based on the Graph Neural Network (GNN) and ARMA model to improve the accuracy of frequency tracking while minimizing algorithm complexity. The experimental results indicate that the fusion of the GNN and the proposed intermittent hypoxia training model can effectively enhance the effects of intermittent hypoxia training in sports.
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