@article{ZHANG2026, 
author = {Yang ZHANG and Lexiang WANG and Mingming JIANG and Naijun CHENG and Tingting SONG and Kunlun HE},
title = {Research progress in multimodal data-driven evaluation models for plateau adaptability of military personnel},
year = {2026},
journal = {Military Medical Sciences},
volume = {50},
number = {3},
pages = {216-220},
keywords = {multimodal data, acute mountain sickness, evaluation model, military personnel, plateau adaptability, operational capability},
url = {https://www.sciopen.com/article/10.7644/j.issn.1674-9960.2025-00252},
doi = {10.7644/j.issn.1674-9960.2025-00252},
abstract = {The plateau environment poses significant challenges to the physiology and operational capability of military personnel, which is also a key contributor to non-combat casualties. Traditional evaluation methods using single-modal indicators lack dynamic monitoring capabilities and fail to capture complex dynamic stress reactions. This paper reviews the research progress in multimodal data-driven evaluation models for plateau adaptability by analyzing the pathological mechanisms and influencing factors of plateau adaptability and outlining the limitations of current standards and single-modal machine learning. The construction of multimodal data-driven models is explored, focusing on both data architectures that combine physiological, behavioral and environmental modalities for military scenarios and cross-modal alignment and fusion techniques. Furthermore, military applications such as dynamic early warning of non-combat casualties, personalized acclimatization and intelligent military health support are described. Finally, future developments in multimodal data-driven evaluation models for plateau adaptability are predicted in hopes of contributing to the combat effectiveness of troops on the plateau.}
}