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基于数据驱动的 PERCLOS 检测的工作类型对空中交通管制员疲劳影响的现场研究。

A Field Study of Work Type Influence on Air Traffic Controllers' Fatigue Based on Data-Driven PERCLOS Detection.

机构信息

The Second Research Institute of Civil Aviation Administration of China, Chengdu 610041, China.

出版信息

Int J Environ Res Public Health. 2021 Nov 13;18(22):11937. doi: 10.3390/ijerph182211937.

DOI:10.3390/ijerph182211937
PMID:34831695
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8622576/
Abstract

The fatigue of air traffic controllers (ATCOs) on duty seriously threatens air traffic safety and needs to be managed. ATCOs perform several different types of work, with each type of work having different characteristics. Nonetheless, the influence of work type on an ATCO's fatigue has yet to be demonstrated. Here, we present a field study in which the fatigue of ATCOs working in two types of work was compared based on an optimized data-driven method that was employed to detect the percentage of eyelid closure over the pupil over time (PERCLOS). Sixty-seven ATCOs working within two typical jobs (i.e., from the terminal control unit (TCU) and area control unit (ACU)) were recruited, and their fatigue was detected immediately before and after shift work using PERCLOS. Using a Spearman correlation test analysis, the results showed that the influence of work type on an ATCO's fatigue had interesting trends. Specifically, the ATCOs at the TCU who handle departures and arrivals, which include converging with and maneuvering around conflicts, retain normal circadian rhythms. Their fatigue was significantly influenced by the various demands from tasks focusing on sequencing and conflict resolution and by the time phase of a normal circadian rhythm. At the ACU, ATCOs manage flights that are mainly on route, causing monotonous monitoring and routine reporting tasks, and the ATCOs generally have frequent night shifts to handle overflights. Their fatigue was significantly influenced by the demand characteristics from tasks, but changes in fatigue rule were not consistent with a normal circadian rhythm, revealing that the ATCOs' circadian rhythms may have already been slightly disturbed. Furthermore, the interactions between task demand and circadian rhythm with an ATCO's fatigue were significantly observed in ATCOs working in the TCU but not in those in the ACU. This study provides first evidence that an ATCO's work type influences his or her fatigue. This discovery may incite stakeholders to consider work type in the management of employee fatigue, not only in the civil aviation industry but also in other transport industries.

摘要

管制员(ATCO)的轮班疲劳严重威胁着空中交通安全,需要加以管理。ATCO 执行几种不同类型的工作,每种类型的工作都有不同的特点。尽管如此,工作类型对 ATCO 疲劳的影响尚未得到证实。在这里,我们进行了一项现场研究,根据一种优化的数据驱动方法,比较了在两种典型工作类型下工作的 ATCO 的疲劳情况,该方法用于检测随时间推移的眼睑对瞳孔的闭合百分比(PERCLOS)。招募了 67 名在两个典型岗位(即终端管制单位(TCU)和区域管制单位(ACU))工作的 ATCO,并使用 PERCLOS 在轮班前后立即检测他们的疲劳情况。通过 Spearman 相关系数检验分析,结果表明工作类型对 ATCO 疲劳的影响具有有趣的趋势。具体来说,处理进近和离场的 TCU 管制员包括与冲突汇聚和机动,保持正常的昼夜节律。他们的疲劳受到关注排序和冲突解决任务的各种需求以及正常昼夜节律的时间阶段的显著影响。在 ACU,ATCO 管理主要在航线上的航班,导致单调的监控和例行报告任务,ATCO 通常有频繁的夜班来处理飞越航班。他们的疲劳受到任务需求特征的显著影响,但疲劳规律的变化与正常昼夜节律不一致,表明 ATCO 的昼夜节律可能已经受到轻微干扰。此外,在 TCU 工作的 ATCO 中,任务需求与昼夜节律与 ATCO 疲劳之间的相互作用非常明显,但在 ACU 工作的 ATCO 中则没有。本研究首次提供了 ATCO 的工作类型会影响其疲劳的证据。这一发现可能促使利益相关者在管理员工疲劳时考虑工作类型,不仅在民航业,而且在其他运输业也是如此。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e4c/8622576/f3f469d63f6a/ijerph-18-11937-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e4c/8622576/90b06fd5c026/ijerph-18-11937-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e4c/8622576/034a27198d25/ijerph-18-11937-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e4c/8622576/4b3dadd80e5f/ijerph-18-11937-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e4c/8622576/7679c1cb5b02/ijerph-18-11937-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e4c/8622576/a88da42320dc/ijerph-18-11937-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e4c/8622576/18f05efaefcf/ijerph-18-11937-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e4c/8622576/9c1d371038e3/ijerph-18-11937-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e4c/8622576/f3f469d63f6a/ijerph-18-11937-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e4c/8622576/90b06fd5c026/ijerph-18-11937-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e4c/8622576/034a27198d25/ijerph-18-11937-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e4c/8622576/4b3dadd80e5f/ijerph-18-11937-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e4c/8622576/7679c1cb5b02/ijerph-18-11937-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e4c/8622576/a88da42320dc/ijerph-18-11937-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e4c/8622576/18f05efaefcf/ijerph-18-11937-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e4c/8622576/9c1d371038e3/ijerph-18-11937-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e4c/8622576/f3f469d63f6a/ijerph-18-11937-g008.jpg

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