数据驱动的间歇低氧训练贝叶斯优化决策方法

The rapid economic development of Qinghai-Tibet region has led to an increasing number of groups entering the plateau, and the consequent problem of high-altitude health has become increasingly prominent. Intermittent hypoxic training (IHT) is a commonly-used preacclimatization approach before rapidly going to the plateau. It is usually designed as fixed open-loop strategies for different individuals, which has several disadvantages such as no standard formulation, lack of systematic theoretical guidance and poor efficacy. In this paper, a data-driven Bayesian closed-loop learning optimization framework of IHT strategy is designed by using small samples, and a Gaussian process model with autoregressive structure of peripheral oxygen saturation (SpO₂) is built for prediction. Based on the predictive model, a risk asymmetric cost function related to the oxygen concentration rate and its direction is developed. Finally, a Bayesian optimization method with safety constraints is proposed to enable the optimal decision of IHT oxygen concentration. Given that the existing simulator cannot reflect the process dynamics of individuals, a reasonable model adaptation law is designed according to the “optimal rate theory”. The feasibility and effectiveness of the proposed closed-loop intervention method are verified by the simulator. These results indicate that the proposed learning framework can help individuals to improve their adaptability to high-altitudes, reduce their non-adaptive adverse reactions in the pretraining stage, and provide precise control solution to personalized IHT.