Fatigue Assessment: Subjective Peer-to-Peer Fatigue Scoring

Gaydos, Steven; Curry, Ian P; Bushby, Alaistair

doi:10.3357/asem.3728.2013

Cited by 5 publications

(6 citation statements)

References 1 publication

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“…Objective assessments of performance decline due to fatigue hold significant scientific merit in terms of reliability and validity. However, translating various laboratory-based psychomotor or neurocognitive tests into practical tools for quantifying risk assessment presents challenges, particularly when it comes to the diverse skill sets required for ensuring flight safety (Gaydos et al, 2013). Subjective rating scales also hold scientific value, as there are numerous self-reported fatigue assessment instruments with different levels of complexity and accuracy.…”

Section: Subjective Measures Of Fatiguementioning

confidence: 99%

“…Another approach involves using peer-rating scales. (Gaydos et al, 2013) initiated a new peer-to-peer system for rating fatigue subjectively, where pilots anonymously provide a weekly (or other appropriate time interval) score to rate the fatigue of all other pilots in the unit. The scoring system uses a simple 1-10 Likert-type scale with specific instructions for each rank to ensure consistent subjective assessment.…”

Section: Two Well-known and Validated Subjective Scales For Measuring...mentioning

confidence: 99%

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A Comprehensive Literature Review on Crew Fatigue in the Aviation Sector

2024

IJFMR

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Causes of human fatigue, such as lack of sleep, disruptions to the body's natural rhythm, health issues, and work-related factors, can have negative impacts on decision-making abilities, memory function, judgment skills, reaction time and awareness in aviation operations. These effects may lead to accidents. This comprehensive study aims to analyze the origins, repercussions, assessment methods and strategies for handling fatigue and associated risks in airline operations using a thorough review of existing literature from both academic and industry sources. The research included peer-reviewed articles as well as publications from various stakeholders within the aviation sector that addressed this topic. It was revealed that fatigue-related risks in aviation are multifaceted and at times unclear. Specifically noted was a significant increase in risk with workdays longer than 16 hours; insufficient pre-shift sleep durations of less than 6 hours; or when work coincides with regular sleep patterns for crew members. Additionally identified were aspects requiring further investigation related to these risks. The conclusion presents recommendations for future studies aimed at reducing these inherent dangers.

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Section: Subjective Measures Of Fatiguementioning

confidence: 99%

Section: Two Well-known and Validated Subjective Scales For Measuring...mentioning

confidence: 99%

A Comprehensive Literature Review on Crew Fatigue in the Aviation Sector

2024

IJFMR

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“…Fatigue can also negatively affect the pilot's performance, decreasing attention capacity and flexibility (Caldwell, 1997;Farrell et al, 2016). Landing is usually the final task for the pilot coming back from a mission, and it requires an increased cognitive effort after a less demanding cruising period (Gaydos et al, 2013;Hartzler, 2014).…”

Section: Pilot Factorsmentioning

confidence: 99%

Helicopter Pilots’ Tasks, Subjective Workload, and the Role of External Visual Cues During Shipboard Landing

Tušl

Rainieri

Fraboni

et al. 2020

Journal of Cognitive Engineering and Decision Making

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Helicopter shipboard landing is a cognitively complex task that is challenging both for pilots and their crew. Effective communication, accurate reading of the flight instruments, as well as monitoring of the external environment are crucial for a successful landing. In particular, the final phases of landing are critical as they imply high workload situations in an unstable environment with restricted space. In the present qualitative study, we interviewed ten helicopter pilots from the Italian Navy using an applied cognitive task analysis approach. We aimed to obtain a detailed description of the landing procedure, and to identify relevant factors that affect pilots’ workload, performance, and safety. Based on the content analysis of the interviews, we have identified six distinct phases of approaching and landing on a ship deck and four categories of factors that may significantly affect pilots’ performance and safety of the landing procedure. Consistent with previous studies, our findings suggest that external visual cueing is vital for a successful landing, in particular during the last phases of landing. Therefore, based on the pilots’ statements, we provide suggestions for possible improvements of external visual cues that have the potential to reduce pilots’ workload and improve the overall safety of landing operations.

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“…Courtney et al conducted sleep restriction and deprivation experiments and concluded that all scales are effective for fatigue detection [2]. Gaydos et al [3] proposed an approach not only based on pilots themselves but also their peers' perspectives. Useche et al [4] studied the relationships among fatigue, work-related and stress-related conditions, and dangerous driving behaviors.…”

Section: Introductionmentioning

confidence: 99%

Research on Railway Dispatcher Fatigue Detection Method Based on Deep Learning with Multi-Feature Fusion

Chen

Zheng

2023

Electronics

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Traffic command and scheduling are the core monitoring aspects of railway transportation. Detecting the fatigued state of dispatchers is, therefore, of great significance to ensure the safety of railway operations. In this paper, we present a multi-feature fatigue detection method based on key points of the human face and body posture. Considering unfavorable factors such as facial occlusion and angle changes that have limited single-feature fatigue state detection methods, we developed our model based on the fusion of body postures and facial features for better accuracy. Using facial key points and eye features, we calculate the percentage of eye closure that accounts for more than 80% of the time duration, as well as blinking and yawning frequency, and we analyze fatigue behaviors, such as yawning, a bowed head (that could indicate sleep state), and lying down on a table, using a behavior recognition algorithm. We fuse five facial features and behavioral postures to comprehensively determine the fatigue state of dispatchers. The results show that on the 300 W dataset, as well as a hand-crafted dataset, the inference time of the improved facial key point detection algorithm based on the retina–face model was 100 ms and that the normalized average error (NME) was 3.58. On our own dataset, the classification accuracy based the an Bi-LSTM-SVM adaptive enhancement algorithm model reached 97%. Video data of volunteers who carried out scheduling operations in the simulation laboratory were used for our experiments, and our multi-feature fusion fatigue detection algorithm showed an accuracy rate of 96.30% and a recall rate of 96.30% in fatigue classification, both of which were higher than those of existing single-feature detection methods. Our multi-feature fatigue detection method offers a potential solution for fatigue level classification in vital areas of the industry, such as in railway transportation.

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Fatigue Assessment: Subjective Peer-to-Peer Fatigue Scoring

Cited by 5 publications

References 1 publication

A Comprehensive Literature Review on Crew Fatigue in the Aviation Sector

A Comprehensive Literature Review on Crew Fatigue in the Aviation Sector

Helicopter Pilots’ Tasks, Subjective Workload, and the Role of External Visual Cues During Shipboard Landing

Research on Railway Dispatcher Fatigue Detection Method Based on Deep Learning with Multi-Feature Fusion

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