Evidence map›Paper›PMID 40596135›Full record

ArticleScientific reports2025

Machine learning evaluation model of pilot workload in a low-visibility environment.

Yuansheng Wang, Xinyao Guo, Shaoshuai Guo, Fang Jiang, Zhuping Liang, Le Peng, Yi Chai

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yuansheng WangSchool of Environmental and Municipal Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
Xinyao GuoHenan International Joint Laboratory of Man Machine Environment and Emergency Management, Anyang Institute of Technology, Anyang, 455099, China. xyguo@zua.edu.cn.
Shaoshuai GuoSchool of Environmental and Municipal Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
Fang JiangCollege of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo, 454000, China.
Zhuping LiangCollege of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo, 454000, China.
Le PengHenan International Joint Laboratory of Man Machine Environment and Emergency Management, Anyang Institute of Technology, Anyang, 455099, China.
Yi ChaiHenan International Joint Laboratory of Man Machine Environment and Emergency Management, Anyang Institute of Technology, Anyang, 455099, China.

Funding

Henan Association for Science and Technology, China 2024HYTP036Office of the Postdoctoral Management Committee of Henan Province，China 202103101Science and Technology Department of Henan Province, China 221111321000Science and Technology Department of Henan Province, China 242102321038
6 · The paper itself

Abstract

To analyze the variation trend of pilots' workload in a low-visibility flight environment and then put forward a scientific evaluation method, this study set up an experimental platform using an E01-pro simulated flight platform and a PhysioPlux multipurpose physiological index tester. The ECG signal data of 40 pilots in normal- and low-visibility flight environments were monitored and collected. Meanwhile, the workload and heart rate (HR) changes of pilots under different visibility levels were analyzed in accordance with the American NASA-TLX workload scale, and the sensitive indexes significantly affecting pilots' workload among ECG signal indexes were screened and extracted. A quantitative evaluation method for pilots' workload was established on the basis of the hidden Markov model (HMM) in machine learning theory, sensitive ECG signal indexes, and subjective scale data. In addition, the workload state of pilots under different visibility levels was judged, and the evolution laws of ECG indexes were revealed. Results show that multiple indexes such as pilots' average HR and scale scores grow significantly under the low-visibility flight environment. The four indexes-pNN20, HF/LF, SD2/SD1, and HR-in ECG signals exhibit significant differences in distinguishing different levels of workload. The accuracy of the HMM-based pilots' workload evaluation model can reach as high as 87.5%. The conclusions provide methodological support for the fast evaluation of pilots' workload state.

Indexed as

Machine LearningPilotsWorkloadAdultElectrocardiographyHeart RateHumansMaleMarkov ChainsCivil pilotsECG signalLow visibilityMachine learningWorkload

Identifiers

PMID40596135
PMCPMC12217077

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.