TY - JOUR
T1 - 数据驱动的间歇低氧训练贝叶斯优化决策方法
AU - Chen, Jing
AU - Shi, Da Wei
AU - Cai, De Heng
AU - Wang, Jun Zheng
AU - Zhu, Ling Ling
N1 - Publisher Copyright:
© 2023 Science Press. All rights reserved.
PY - 2023/8
Y1 - 2023/8
N2 - 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.
AB - 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.
KW - Bayesian optimization
KW - Data-driven control
KW - Gaussian process
KW - high-altitude adaptability improvement
KW - intermittent hypoxic training (IHT)
KW - risk asymmetric cost function
UR - http://www.scopus.com/inward/record.url?scp=85184058282&partnerID=8YFLogxK
U2 - 10.16383/j.aas.c220712
DO - 10.16383/j.aas.c220712
M3 - 文章
AN - SCOPUS:85184058282
SN - 0254-4156
VL - 49
SP - 1667
EP - 1678
JO - Zidonghua Xuebao/Acta Automatica Sinica
JF - Zidonghua Xuebao/Acta Automatica Sinica
IS - 8
ER -