A multidimensional performance evaluation model for human in overweight environment based on artificial neural networks

LIU Min; GONG Xianwen; LI Nailiang; SHI Yusheng; ZHAO Dongming; XIE Lan; JIANG Yuan; ZHENG Jian; WU Bin; HUANG Weifen

doi:10.16289/j.cnki.1002-0837.2025.06004

Abstract：Overweight environments have many impacts on human physiology,emotions,and cognitive functions.The overweight environment of manned lunar landing and deep space exploration missions is more complex,which puts higher demands on the psychological quality and operational ability of astronauts.In order to objectively and accurately evaluate human efficacy in overweight environments,an artificial neural network Back Propagation(BP algorithm)was used to construct an efficacy evaluation model based on human physiology,emotions,and cognitive abilities in overweight environments.After verification and comparison,a four-layer artificial neural network with 2 hidden layers,29 input nodes,and 1 output node was ultimately selected.The model accuracy of the experimental sample reached 93.55%,and the accuracy after data augmentation reached 98.3%.This model can achieve automated evaluation of subjects in overweight environments,reduce the need for subjective evaluation by experts,weaken the dependence on subjective judgments of main subjects,and provide more comprehensive and accurate quantitative evaluation of human abilities in overweight environments.It provides technical support for the subsequent use of artificial intelligence technology to establish an auxiliary decision-making system for overweight main subjects/main officials.Subsequently,an efficacy evaluation model based on human physiology,emotions,and cognitive abilities in overweight environments can be constructed,and an auxiliary decision-making system for overweight experiments/bishops can be established using artificial intelligence technology.Provide support for more efficient and accurate selection,training,or identification of overweight astronauts and pilots.