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Anais Pontiggia1, Michael Quiquempoix1, Pierre Fabries2

  • 1Institut de recherche biomédicale des armées (IRBA), Brétigny sur Orge, France; UMR 7330 VIFASOM, Université Paris Cité, Paris, France.

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まとめ
この要約は機械生成です。

睡眠制限や低酸素症などの多様な条件下でのトレーニングを必要とするパイロットの精神的ワークロード（MW）を予測する機械学習モデル。マルチモーダル生理学的データは、航空安全のためのモデルの堅牢性を向上させます。

キーワード:
交差検証ECGEEGアイトラッキング低酸素症精神的ワークロード睡眠制限教師あり機械学習

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科学分野:

  • 航空生理学
  • ヘルスケアにおける機械学習
  • ヒューマンファクター工学

背景:

  • パイロットは、低酸素症および睡眠制限によって悪化する高い精神的ワークロード（MW）に直面しています。
  • 既存のMW予測モデルは、さまざまな生理学的状態にわたる堅牢性が欠けている可能性があります。

研究 の 目的:

  • 低酸素症および睡眠制限下での機械学習MW予測モデルの交差検証。
  • 妥当性を向上させるためのマルチモーダル生理学的データを使用した堅牢なMW予測モデルの開発。

主な方法:

  • 17人の参加者が、制御された睡眠制限（SR）または習慣的な睡眠（HS）および低酸素症（HY）または正常酸素（NO）を受けました。
  • 精神的ワークロードは、Multi-Attribute Test Battery（MATB）-IIおよび聴覚タスクを使用して操作されました。
  • 機械学習分類器は、EEG、ECG、呼吸、およびアイトラッキングセンサーからの特徴を使用して評価されました。

主要な成果:

  • 個々のモデルは、習慣的な睡眠および正常酸素（例：KNN F1スコア80.3%）で最良のパフォーマンスを発揮しました。
  • モデルパフォーマンスは、睡眠制限および/または低酸素症（F1 <35%）で著しく低下しました。
  • すべての条件からのデータでトレーニングされたモデル、特にEEGおよびアイトラッキングを組み込んだモデルは、条件間のパフォーマンスを向上させました（例：KNN F1スコア77.4%）。

結論:

  • さまざまな生理学的条件で機械学習モデルをトレーニングすることは、堅牢なMW予測に不可欠です。
  • マルチモーダル生理学的データは、航空におけるMW予測モデルの妥当性と信頼性を高めます。