我们已经启动了:使用人工智能来确定未来太空任务的适应性概况的发现研究
Barbara Le Roy1, Damien Claverie2, Lucas Sauvadet3
1Human Adaptation Institute, Marseille, France.
Physiological reports
|August 30, 2025
概括
人工智能可以使用心率变化 (HRV) 数据预测宇航员的适应情况. 这种机器学习方法可以识别不同的生理反应,
科学领域:
- 太空医学
- 人类生理学
- 人工智能
背景情况:
- 长时间的太空任务需要宇航员有很大的适应能力.
- 内接和心率变化是生理适应和健康的关键指标.
- 预测个体适应对于宇航员的福祉和任务的成功至关重要.
研究的目的:
- 研究人工智能在预测宇航员适应能力方面的应用.
- 在各种空间模拟环境中分析整体感应和HRV反应.
- 开发个体宇航员适应的预测模型.
主要方法:
- 在四个空间模拟环境中从84名参与者收集的数据 (抛物线飞行,潜艇,海上生存,危险演习).
- 通过集群测量受体间敏感度和HRV以确定适应性.
- 在基线数据上训练的支持向量机 (SVM) 模型,以预测已识别的配置文件.
主要成果:
- 确定了三个不同的适应特征,其特点是整体感知意识,身心融合和神经感知的差异.
- 在预测这些适应特征时,SVM模型的准确性达到了79%.
- 这些发现突显了适应的体现性和HRV中整体受体路径的作用.
结论:
- 机器学习可以有效地预测宇航员的适应结果,
- 这种方法提供了一个可行的方法来预测个体对苛刻环境的反应.
- 结果支持根据个性化的生理特征量身定制宇航员培训, 以提高任务准备.
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