Computational Modeling of Individual Differences in Cardiovascular Response during Parabolic Flight

Hrudayavani S Vellore, R. Galvan-Garza, A. Diaz-Artiles
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Abstract

Future human aerospace systems will consist of a complex integration of multiple technologies, including Artificial Intelligence (AI) driven autonomy. We expect autonomous systems to support and augment human performance, especially when humans are experiencing physiological or cognitive decrements in operational scenarios. To accomplish this, we need the ability to identify these human performance decrements in different individuals and in different mission contexts (e.g., varying g-levels, acceleration profiles). Factors such as sex, weight, and height can alter physiological response to various stressors. Therefore, accounting for these differences is essential to build effective autonomous systems in these operational contexts. Computational models and algorithms that drive our human assessment systems are rooted in theory but also need realistic data for testing and evaluation. While there is a basic understanding of expected changes in physiology under varying stressors, available data are largely lab-based and sparse. However, experimentally determining the response of each individual in a large variety of mission contexts is prohibitively expensive and time-consuming. Thus, future human assessment algorithm development efforts would substantially benefit from simulated, representative datasets created through configurable human models. We will build on prior cardiovascular, metabolic, and other modeling work to create a physiological model tailorable to specific operational mission contexts and personalized input parameters. In particular, we have implemented a 21-compartment lumped-parameter model to simulate physiological responses of a 50th percentile female and a 50th percentile male during a parabolic flight maneuver. The modeled individuals were differentiated by anthropometric and total blood volume data based on U.S. Army personnel. Results of the simulations highlight and quantify the differences in physiological responses between the two individuals when exposed to the same parabolic fight maneuver. Differences between the male and female models were greatest during hypergravity for almost all parameters except for Stroke Volume (SV), which presented the greatest differences between the two individuals during the transition between hypergravity and 1g. Our preliminary modeling effort demonstrates that differences exist in the cardiovascular response between two simulated, anthropometrically different individuals during a parabolic flight maneuver. In addition, those physiological differences are dependent on the magnitude of the gravity level. These results support and further justify the need for individualized modeling.
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抛物飞行中心血管反应个体差异的计算建模
未来的人类航空航天系统将由多种技术的复杂集成组成,包括人工智能(AI)驱动的自主性。我们期望自主系统能够支持和增强人类的表现,特别是当人类在操作场景中经历生理或认知衰退时。为了实现这一目标,我们需要有能力在不同的个体和不同的任务环境(例如,不同的g水平,加速度曲线)中识别这些人类性能下降。性别、体重和身高等因素可以改变对各种压力源的生理反应。因此,考虑这些差异对于在这些操作环境中构建有效的自治系统至关重要。驱动人类评估系统的计算模型和算法植根于理论,但也需要实际数据进行测试和评估。虽然对不同应激源下的生理预期变化有基本的了解,但可用的数据主要是基于实验室的,而且很少。然而,通过实验确定每个人在各种各样的任务环境中的反应是非常昂贵和耗时的。因此,未来人类评估算法的开发工作将大大受益于通过可配置的人类模型创建的模拟的、代表性的数据集。我们将在先前的心血管、代谢和其他建模工作的基础上,创建一个适合特定作战任务背景和个性化输入参数的生理模型。特别是,我们已经实现了一个21室集总参数模型来模拟第50百分位女性和第50百分位男性在抛物线飞行机动中的生理反应。模型个体通过基于美国陆军人员的人体测量和总血量数据进行区分。模拟结果强调并量化了两个人在暴露于相同的抛物线战斗机动时生理反应的差异。在超重力状态下,除SV外,几乎所有参数的差异都最大,其中SV在从超重力状态过渡到1g状态时差异最大。我们的初步建模工作表明,在抛物线飞行机动过程中,两个模拟的、人体测量不同的个体之间的心血管反应存在差异。此外,这些生理差异取决于重力水平的大小。这些结果支持并进一步证明了个性化建模的必要性。
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