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The analysis of multi-modal data, especially related to such a complex object as a human in the loop of human-system integration can be more effective if applying the data analytics methodology. In our research we studied indicators: human performance of regular cognitive tests over 6 weeks, physiological measurements of HRV and blood pressure, as well as external indices of atmospheric pressure, solar wind, inter-planet and geomagnetic fields. This research proposes the technique based on descriptive analysis of human indicators of cognitive performance including comparison of variations of indicators at levels of a human and inter-human variation; multiple regression analysis with the stepwise procedure to select the most informative indicators among potentially influencing factors; summarizing data visualized representation of revealed indicators that can be included in descriptive models. It has been revealed that the most influences indicators are HRV, total strength of the interplanetary magnetic field Bt, SW density and anisotropic index.

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Using Data Analytics to Study the Multi-modal Influence of the Environment on Human Cognitive Performance

  • 会议论文

  • 发表日期：

    2026年

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  • Oleksandr Burov,
  • Svitlana Lytvynova,
  • Olha Pinchuk,
  • Olena Kuzminska,
  • Oleksii Tkachenko,
  • Natalia Kovalenko,
  • Svitlana Agadjanova

摘要

The analysis of multi-modal data, especially related to such a complex object as a human in the loop of human-system integration can be more effective if applying the data analytics methodology. In our research we studied indicators: human performance of regular cognitive tests over 6 weeks, physiological measurements of HRV and blood pressure, as well as external indices of atmospheric pressure, solar wind, inter-planet and geomagnetic fields. This research proposes the technique based on descriptive analysis of human indicators of cognitive performance including comparison of variations of indicators at levels of a human and inter-human variation; multiple regression analysis with the stepwise procedure to select the most informative indicators among potentially influencing factors; summarizing data visualized representation of revealed indicators that can be included in descriptive models. It has been revealed that the most influences indicators are HRV, total strength of the interplanetary magnetic field Bt, SW density and anisotropic index.