航空合作目标评估:在模拟环境中验证和比较两种新方法

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Rossella Capotorto, Vincenzo Ronca, Nicolina Sciaraffa, Gianluca Borghini, Gianluca Di Flumeri, Lorenzo Mezzadri, Alessia Vozzi, Andrea Giorgi, Daniele Germano, Fabio Babiloni, Pietro Aricò
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引用次数: 0

摘要

导言在操作环境中,人与人之间的互动与合作对效率和安全至关重要。这些状态受到个人认知和情绪状态的影响。人因研究旨在客观量化这些状态,以防止人为失误并保持稳定的性能,特别是在航空等高风险环境中,人为失误和性能占事故的很大一部分。方法因此,本研究旨在评估和验证两种新方法,用于评估参与真实飞行模拟任务的专业飞行员之间的合作程度。此外,该研究还旨在评估所提出的指标是否能够根据合作程度来区分机组人员的专业水平。八名机组人员参与了实验,其中四名为无经验飞行员,四名为有经验飞行员。一名专家培训师一边模拟空中交通管理通信,一边作为主题专家,对飞行员在模拟过程中的心理状态进行外部评估。本研究中引入的两种新方法是基于循环相关和互信息技术制定的。此外,还发现有经验的飞行员与无经验的飞行员相比,合作时间明显较长(p &p;lt;0.05)。此外,这些初步结果与任务期间每 30 秒收集一次的主观和行为测量结果有明显的相关性(p & lt; 0.05),证实了其可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cooperation objective evaluation in aviation: validation and comparison of two novel approaches in simulated environment
IntroductionIn operational environments, human interaction and cooperation between individuals are critical to efficiency and safety. These states are influenced by individuals' cognitive and emotional states. Human factor research aims to objectively quantify these states to prevent human error and maintain constant performances, particularly in high-risk settings such as aviation, where human error and performance account for a significant portion of accidents.MethodsThus, this study aimed to evaluate and validate two novel methods for assessing the degree of cooperation among professional pilots engaged in real-flight simulation tasks. In addition, the study aimed to assess the ability of the proposed metrics to differentiate between the expertise levels of operating crews based on their levels of cooperation. Eight crews were involved in the experiments, consisting of four crews of Unexperienced pilots and four crews of Experienced pilots. An expert trainer, simulating air traffic management communication on one side and acting as a subject matter expert on the other, provided external evaluations of the pilots' mental states during the simulation. The two novel approaches introduced in this study were formulated based on circular correlation and mutual information techniques.Results and discussionThe findings demonstrated the possibility of quantifying cooperation levels among pilots during realistic flight simulations. In addition, cooperation time is found to be significantly higher (p &lt; 0.05) among Experienced pilots compared to Unexperienced ones. Furthermore, these preliminary results exhibited significant correlations (p &lt; 0.05) with subjective and behavioral measures collected every 30 s during the task, confirming their reliability.
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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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