ArticleBiosensors2022
Pilot Behavior Recognition Based on Multi-Modality Fusion Technology Using Physiological Characteristics.
Article in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Comparative analysis of machine-learning methods for prediction of pilot performance during startle events from neuropsychophysiological features of stress resilience and cognitive task scores.Frontiers in physiology · 2026Article
- Human limits in next-generation fighter aviation: psychophysiological stressors, readiness, health, performance, and countermeasures.Frontiers in physiology · 2026Review
- Temporal Relation Modeling and Multimodal Adversarial Alignment Network for Pilot Workload Evaluation.IEEE journal of translational engineering in health and medicine · 2025Article
- Assessment of flight fatigue using heart rate variability and machine learning approaches.Frontiers in neuroscience · 2025Article
- Stress and Workload Assessment in Aviation-A Narrative Review.Sensors (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
With the development of the autopilot system, the main task of a pilot has changed from controlling the aircraft to supervising the autopilot system and making critical decisions. Therefore, the human-machine interaction system needs to be improved accordingly. A key step to improving the human-machine interaction system is to improve its understanding of the pilots' status, including fatigue, stress, workload, etc. Monitoring pilots' status can effectively prevent human error and achieve optimal human-machine collaboration. As such, there is a need to recognize pilots' status and predict the behaviors responsible for changes of state. For this purpose, in this study, 14 Air Force cadets fly in an F-35 Lightning II Joint Strike Fighter simulator through a series of maneuvers involving takeoff, level flight, turn and hover, roll, somersault, and stall. Electro cardio (ECG), myoelectricity (EMG), galvanic skin response (GSR), respiration (RESP), and skin temperature (SKT) measurements are derived through wearable physiological data collection devices. Physiological indicators influenced by the pilot's behavioral status are objectively analyzed. Multi-modality fusion technology (MTF) is adopted to fuse these data in the feature layer. Additionally, four classifiers are integrated to identify pilots' behaviors in the strategy layer. The results indicate that MTF can help to recognize pilot behavior in a more comprehensive and precise way.
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What OpenQuestion holds
Registered trials
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.