Research on Fatigue Detection Methods for Aviation Personnel

doi:10.3778/j.issn.1002-8331.2403-0135

Abstract

Abstract: The fatigue problem for aviation personnel has attracted attention in civil aviation, in order to reduce the risk of civil aviation operation caused by fatigue problems, the development status of aviation personnel fatigue research under the field of civil aviation is analyzed, and the current fatigue research results are sorted out and future development of fatigue detection in civil aviation is put forward. Firstly, the concept of fatigue of aviation personnel and its influencing factors are clarified. Then, the fatigue studies of pilots, air traffic controllers and aircraft maintenance personnel are compared and analyzed from the dimensions of physiological indicators and subjective measurement methods, and the fatigue detection models of pilots and air traffic controllers are sorted out and summarized by combining the computerized detection methods. Finally, the shortcomings and development direction of the current fatigue detection are put forward according to the research. The results show that the objective detection methods are gradually becoming mature, and physiological indicators are widely used in fatigue detection. The selection of indicators for fatigue detection models needs to be further studied, and the accuracy of model identification and fatigue classification needs to be improved, especially the fatigue detection model for maintenance personnel needs to be developed. In the future, passive fatigue caused by automated driving and human-machine function allocation problems in highly automated cockpit environment is the focus of research.

Key words: aviation personnel, fatigue detection, physiological indicator, subjective evaluation, discriminant model

摘要： 航空人员疲劳问题一直受到民航业的广泛关注，为减少由疲劳问题带来的民航运行风险，综合分析了民航领域下的航空人员疲劳研究现状，梳理当前疲劳研究成果，为航空领域的疲劳研究提供新的思路。明确了航空人员疲劳概念及其影响因素；从生理指标、主观测评两个检测方法维度对飞行员、空中交通管制员和机务维修人员的疲劳研究进行对比分析，并结合计算机检测方法，对飞行员和空中交通管制员的疲劳检测模型进行梳理总结；根据研究梳理提出当前疲劳检测的不足之处及发展方向。研究结果表明：客观检测手段逐渐趋于成熟，生理指标被广泛应用于疲劳检测中；疲劳检测模型的指标选取有待于进一步研究，模型识别准确性及疲劳分类精度有待提高，尤其是机务人员疲劳检测模型尚不成熟；未来驾驶舱高度自动化环境下的自动驾驶及人机功能分配问题带来的被动疲劳是研究重点。

关键词: 航空人员, 疲劳检测, 生理指标, 主观测评, 判别模型

ZHANG Rong, LIANG Xinyue. Research on Fatigue Detection Methods for Aviation Personnel[J]. Computer Engineering and Applications, 2024, 60(20): 84-95.

张荣, 梁馨月. 航空人员疲劳检测方法研究[J]. 计算机工程与应用, 2024, 60(20): 84-95.

References

[1] RUDARI L, JOHNSON M, GESKE R, et al. Pilot perceptions on impact of crew rest regulations on safety and fatigue[J]. International Journal of Aviation, Aeronautics, and Aerospace, 2016, 3(1): 1-16.
[2] CALDWELL J A, MALLIS M M, CALDWELL J L, et al. Fatigue countermeasures in aviation[J]. Aviation, Space, and Environmental Medicine, 2009, 80(1): 29-59.
[3] DAWSON D, NOY I Y, H?RM? M, et al. Modelling fatigue and the use of fatigue models in work settings[J]. Accident Analysis & Prevention, 2011, 43(2): 549-564.
[4] MAY J F, BALDWIN C L. Driver fatigue: the importance of identifying causal factors of fatigue when considering detection and countermeasure technologies[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2009, 12(3): 218-224.
[5] HILL A S. The Chicago convention[J]. The North American Review, 1868, 107: 167-186.
[6] RASHID H S J, PLACE C S, BRAITHWAITE G R. Eradicating root causes of aviation maintenance errors: introducing the AMMP[J]. Cognition, Technology & Work, 2014, 16(1): 71-90.
[7] 李都厚, 刘群, 袁伟, 等. 疲劳驾驶与交通事故关系[J]. 交通运输工程学报, 2010, 10(2): 104-109.
LI D H, LIU Q, YUAN W, et al. Relationship between fatigue driving and traffic accident[J]. Journal of Traffic and Transportation Engineering, 2010, 10(2): 104-109.
[8] 段振伟, 景国勋, 杨书召. 基于安全人机工程学的驾驶疲劳因素及其产生机理分析[J]. 河南理工大学学报 (自然科学版), 2008, 27(1): 21-27.
DUAN Z W, JING G X, YANG S Z. Analysis of driving fatigue and mechanism based on safety drgonamics[J]. Journal of Henan Polytechnic University (Natural Science), 2008, 27(1): 21-27.
[9] 张瑞, 朱天军, 邹志亮, 等. 驾驶员疲劳驾驶检测方法研究综述[J]. 计算机工程与应用, 2022, 58(21): 53-66.
ZHANG R, ZHU T J, ZOU Z L, et al. Review of research on driver fatigue driving detection methods[J]. Computer Engineering and Applications, 2022, 58(21): 53-66.
[10] BALKIN T J, WESENSTEN N J. Differentiation of sleepiness and mental fatigue effects[M]//Cognitive fatigue: multidisciplinary perspectives on current research and future applications. Washington, DC: American Psychological Association, 2011: 47-66.
[11] WINGELAAR-JAGRT Y Q, WINGELAAR T T, RIEDEL W J, et al. Fatigue in aviation: safety risks, preventive strategies and pharmacological interventions[J]. Frontiers in Physiology, 2021, 12: 712628.
[12] 刘俊杰, 张晴, 于佳楠. 近30年全球商业运输航空主力机型事故对比分析[J]. 安全, 2022, 43(10): 21-28.
LIU J J, ZHANG Q, YU J N. Comparative statistics and analysis of accidents of two main types of commercial transport aviation in recent 30 years[J]. Safety＆Security, 2022, 43(10): 21-28.
[13] KANDERA B, ?KULTéTY F, MESáRO?OVá K. Consequences of flight crew fatigue on the safety of civil aviation[J]. Transportation Research Procedia, 2019, 43: 278-289.
[14] VERDIèRE K J, ROY R N, DEHAIS F. Detecting pilot’s engagement using fNIRS connectivity features in an automated vs. manual landing scenario[J]. Frontiers in Human Neuroscience, 2018, 12: 6.
[15] HAMANN A, CARSTENGERDES N. Assessing the development of mental fatigue during simulated flights with concurrent EEG-fNIRS measurement[J]. Scientific Reports, 2023, 13(1): 4738.
[16] DRAICCHIO F, CHINI G, SILVETTI A, et al. Neck and shoulder muscle fatigue in high performance aircrafts pilots: a case study[C]//Proceedings of the International Conference on Applied Human Factors and Ergonomics, 2019: 40-49.
[17] HANAKOVA L, SOCHA V, SOCHA L, et al. The influence of fatigue on psychophysiological indicators during 24 hours testing of pilots[C]//Proceedings of the 2019 IEEE 17th World Symposium on Applied Machine Intelligence and Informatics, 2019: 181-186.
[18] NAEERI S, MANDAL S, KANG Z. Analyzing pilots’ fatigue for prolonged flight missions: multimodal analysis approach using vigilance test and eye tracking[C]//Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2019: 111-115.
[19] LEE S, KIM J K. Factors contributing to the risk of airline pilot fatigue[J]. Journal of Air Transport Management, 2018, 67: 197-207.
[20] BOURGEOIS-BOUGRINE S, CARBON P, GOUNELLE C, et al. Perceived fatigue for short- and long-haul flights: a survey of 739 airline pilots[J]. Aviation Space and Environmental Medicine, 2003, 74(10): 1072-1077.
[21] BABA M D, DARUIS D D I, NUHMANDEEN B. A survey on sleeping patterns and fatigue among pilots in south east asia[J]. Applied Mechanics and Materials, 2011, 58/60: 715-721.
[22] 裘旭益, 仇峰, 吴奇. 面向飞行员疲劳状态监测的脑认知深度模型研究[J]. 航空电子技术, 2020, 51(4): 13-19.
QIU X Y, QIU F, WU Q. Brain cognitive deep model for pilot fatigue state monitoring[J]. Avionics Technology, 2020, 51(4): 13-19.
[23] 储银雪, 陆智俊, 裘旭益, 等. 深度稀疏自编码网络识别飞行员疲劳状态[J]. 控制理论与应用, 2019, 36(6): 850-857.
CHU Y X, LU Z J, QIU X Y, et al. Using deep sparse auto-encoding network to identify pilots’ fatigue status[J]. Control Theory & Applications, 2019, 36(6): 850-857.
[24] WU E Q, PENGX Y, ZHANG C Z, et al. Pilots’ fatigue status recognition using deep contractive autoencoder network[J]. IEEE Transactions on Instrumentation and Measurement, 2019, 68(10): 3907-3919.
[25] PAN T, WANG H, SI H, et al. Identification of pilots’ fatigue status based on electrocardiogram signals[J]. Sensors, 2021, 21(9): 3003.
[26] XU B, WU Q, Ⅺ C, et al. Recognition of the fatigue status of pilots using BF-PSO optimized multi-class GP classification with sEMG signals[J]. Reliability Engineering & System Safety, 2020, 199: 106930.
[27] HAN S Y, KWAK N S, Oh T, et al. Classification of pilots’ mental states using a multimodal deep learning network[J]. Biocybernetics and Biomedical Engineering, 2020, 40(1): 324-336.
[28] LI Y, LI K, WANG S, et al. Pilot behavior recognition based on multi-modality fusion technology using physiological characteristics[J]. Biosensors, 2022, 12(6): 404.
[29] POWELL D M C, SPENCER M B, PETRIE K J. Comparison of in-flight measures with predictions of a bio-mathematical fatigue model[J]. Aviation, Space, and Environmental Medicine, 2014, 85(12): 1177-1184.
[30] 刘亚威. 管制疲劳的眼动指标研究[D]. 天津: 中国民航大学, 2018.
LIU Y W. The research of eye movement index for detecting air traffic controllers’ fatigue[D]. Tianjin: Civil Aviation University of China, 2018.
[31] MONK T H. Practical consequences of fatigue-related performance failures[J]. Sleep, 2007, 30(11): 1402.
[32] 汪磊, 孙瑞山. 基于面部特征识别的管制员疲劳监测方法研究[J]. 中国安全科学学报, 2012, 22(7): 66-71.
WANG L, SUN R S. Study on face feature recognition-based fatigue monitoring method for air traffic controller[J]. China Safety Science Journal, 2012, 22(7): 66-71.
[33] ZHANG J, CHEN Z, LIU W, et al. A field study of work type influence on air traffic controllers’ fatigue based on data-driven PERCLOS detection[J]. International Journal of Environmental Research and Public Health, 2021, 18(22): 11937.
[34] 靳慧斌, 朱国蕾. 眼动指标检测管制疲劳的有效性[J]. 科学技术与工程, 2018, 18(19): 136-140.
JIN H B, ZHU G L. Effectiveness of eye movement indicators on air traffic controllers’ fatigue detection[J]. Science Technology and Engineering, 2018, 18(19): 136-140.
[35] 卜建, 刘银鑫, 王艳军. 空中交通管制员的眼动行为与疲劳关系[J]. 航空学报, 2017, 38(1): 57-62.
BU J, LIU Y X, WANG Y J. Relationship between air traffic controllers’ eye movement and fatigue[J]. Acta Aeronautica ET Astronautica Sinica, 2017, 38(1): 57-62.
[36] LI Q, NG K K H, YIU C Y, et al. Recognising situation awareness associated with different workloads using EEG and eye-tracking features in air traffic control tasks[J]. Knowledge-Based Systems, 2022, 260: 110179.
[37] DASARI D, SHOU G, DING L. ICA-derived EEG correlates to mental fatigue, effort, and workload in a realistically simulated air traffic control task[J]. Frontiers in Neuroscience, 2017, 11: 297.
[38] CHEN M L, LU S Y, MAO I F. Subjective symptoms and physiological measures of fatigue in air traffic controllers[J]. International Journal of Industrial Ergonomics, 2019, 70: 1-8.
[39] 孙瑞山, 马广福, 袁乐平. 语音反应时特性的管制员疲劳风险分析[J]. 中国安全科学学报, 2016, 26(12): 7-12.
SUN R S, MA G F, YUAN L P. Analysis of risk of controller fatigue based on characteristics of speech reaction time[J]. China Safety Science Journal, 2016, 26(12): 7-12.
[40] WU N, SUN J. Fatigue detection of air traffic controllers based on radiotelephony communications and self-adaption quantum genetic algorithm optimization ensemble learning[J]. Applied Sciences, 2022, 12(20): 10252.
[41] KOUBA P, ?MOTEK M, TICHY T, et al. Detection of air traffic controllers’ fatigue using voice analysis-an EEG validation study[J]. International Journal of Industrial Ergonomics, 2023, 95: 103442.
[42] LI W C, ZHANG J, KEARNEY P. Psychophysiological coherence training to moderate air traffic controllers’ fatigue on rotating roster[J]. Risk Analysis, 2023, 43(2): 391-404.
[43] CHANG Y H, YANG H H, HSU W J. Effects of work shifts on fatigue levels of air traffic controllers[J]. Journal of Air Transport Management, 2019, 76: 1-9.
[44] 孙瑞山, 马广福, 袁乐平. MFI-16管制员疲劳量表的编制及信度效度分析[J]. 职业与健康, 2016, 32(22): 3053-3056.
SUN R S, MA G F, YUAN L P. Revision on multidimensional fatigue inventory for controllers and analysis on reliability and validity analysis[J]. Occupation and Health, 2016, 32(22): 3053-3056.
[45] 孙瑞山, 陈毅, 孙立斌. 基于CFF的管制员疲劳评估方法[J]. 中国安全科学学报, 2022, 32(4): 192-197.
SUN R S, CHEN Y, SUN L B. Fatigue assessment method of controllers based on CFF[J]. China Safety Science Journal, 2022, 32(4): 192-197.
[46] 孙瑞山, 李康, 李敬强. 空中交通管制员疲劳状态及影响因素分析[J]. 安全与环境学报, 2018, 18(6): 2241-2246.
SUN R S, LI K, LI J Q. Analysis of the actual situation and influential factors of the fatigue among the air traffic controllers[J]. Journal of Safety and Environment, 2018, 18(6): 2241-2246.
[47] 李兆悦. 面向管制员语音疲劳判别任务的语音特征提取研究[J]. 航空计算技术, 2020, 50(5): 56-60.
LI Z Y. Acoustic feature extraction for speech fatigue recognition of ATC[J]. Aeronautical Computing Technique, 2020, 50(5): 56-60.
[48] YANG J, YANG H, WU Z, et al. Cognitive load assessment of air traffic controller based on SCNN-TransE network using speech data[J]. Aerospace, 2023, 10(7): 584.
[49] 陈超. 基于深度学习的管制员语音疲劳检测研究[D]. 广汉: 中国民用航空飞行学院, 2023.
CHEN C. Research on controller speech fatigue detection based on deep learning[D]. Guanghan: Civil Aviation Flight University of China, 2023.
[50] 梁海军, 刘长炎, 陈宽明, 等. 基于DCNN的管制员疲劳状态检测[J]. 科学技术与工程, 2021, 21(35): 15277-15283.
LIIANG H J, LIU C Y, CHEN K M, et al. Controller fatigue state detection based on DCNN[J]. Science Technology and Engineering, 2021, 21(35): 15277-15283.
[51] 王超, 徐楚昕, 王志锋. 面向空中交通管制员疲劳识别的哈欠检测[J]. 安全与环境学报, 2023, 23(6): 1970-1977.
WANG C, XU C X, WANG Z F. Research on yawn detection method for fatigue recognition of air traffic controllers[J]. Journal of Safety and Environment, 2023, 23(6): 1970-1977.
[52] BENDAK S, RASHID H S J. Fatigue in aviation: a systematic review of the literature[J]. International Journal of Industrial Ergonomics, 2020, 76: 102928.
[53] AVERSK, JOHNSON W B. A review of federal aviation administration fatigue research[J]. Aviation Psychology and Applied Human Factors, 2011, 1(2): 87-98.
[54] 胡臻. 机务维修人员疲劳风险指数研究[D]. 天津: 中国民航大学, 2016.
HU Z. Research on fatigue risk index of maintenance personnel[D]. Tianjin: Civil Aviation University of China, 2016.
[55] MILLER M, MRUSEK B, HERBIC J. Managing fatigue in aviation maintenance while promoting a human factors safety reporting system; a strategic approach to aviation safety[C]//Proceedings of the Safety Management and Human Factors, 2023: 54-63.
[56] SANTOS L F F M, MELICIO R. Stress, pressure and fatigue on aircraft maintenance personal[J]. International Review of Aerospace Engineering, 2019, 12(1): 35.
[57] 程林. 眼动技术在机务维修中的应用分析[J]. 信息系统工程, 2018(12): 158.
CHENG L. Application analysis of eye movement technology in locomotive maintenance[J]. China CIO News, 2018(12): 158.
[58] 贺强, 谭德强, 程林. 民机复合材料目视检查中的眼动与疲劳检测[J]. 航空学报, 2020, 41(5): 211-219.
HE Q, TAN D Q, CHENG L. Eye movement and fatigue detection in visual inspection of civil aircraft composite materials[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(5): 211-219.
[59] 刘洋, 曹新生, 文治洪, 等. 长时间颈部后仰相关局部肌肉表面肌电特征分析[J]. 北京生物医学工程, 2023, 42(6): 581-588.
LIU Y, CAO X S, WEN Z H, et al. Analysis of surface electromyographic characteristics of local muscles related to long-term neck tilt back[J]. Beijing Biomedical Engineering, 2023, 42(6): 581-588.
[60] HOBBS A, AVERS B K, HILES J J, et al. Fatigue risk management in aviation maintenance: current best practices and potential future countermeasures[EB/OL]. (2011-06-01)[2023-03-09]. https://rosap.ntl.bts.gov/view/dot/20808.
[61] ASAD H, YU D, MOTT J H. Risk factors for musculoskeletal injuries in airline maintenance, repair & overhaul[J]. International Journal of Industrial Ergonomics, 2019, 70: 107-115.
[62] RODRIGURZ F H A, MAYORGA O V A. Characterization of low back pain in pilots and maintenance technicians on a commercial airline[J]. Aerospace Medicine and Human Performance, 2016, 87(9): 795-799.
[63] YAZGAN E, OZKAN N F, ULUTAS B H. A questionnaire-based musculoskeletal disorder assessment for aircraft maintenance technicians[J]. Aircraft Engineering and Aerospace Technology, 2021, 94(2): 240-247.
[64] 张晓全, 潘晶, 王欢, 等. 机务人员疲劳致因影响分析[J]. 中国安全科学学报, 2013, 23(2): 97-102.
ZHANG X Q, PAN J, WANG H, et al. Analysis of fatigue causes for aircraft maintenance personnel[J]. China Safety Science Journal, 2013, 23(2): 97-102.
[65] 孙瑞山, 胡臻, 汪磊, 等. 基于权的最小平方法和熵权法的机务维修人员疲劳影响因素研究[J]. 安全与环境工程, 2016, 23(3): 167-170.
SUN R S, HU Z, WANG L, et al. Influencing factors of maintenance personnel fatigue based on WLSM and entropy weight method[J]. Safety snd Environment Engineering, 2016, 23(3): 167-170.
[66] 汪磊, 任勇. 机务维修人员疲劳风险评价模型及管理系统实现[J]. 中国安全科学学报, 2017, 27(5): 70-75.
WANG L, REN Y. Fatigue risk evaluation model and system for civil aviation maintenance personnel[J]. China Safety Science Journal, 2017, 27(5): 70-75.
[67] SIGNAL T L, BERG V D M J, MULRINE H M. Personal and work factors that predict fatigue-related errors in aircraft maintenance engineering[J]. Aerospace Medicine and Human Performance, 2019, 90(10): 860-866.
[68] ZIMMERMANN N, WANG P, PULLEN K. Fatigue in aircraft maintenance technician schools[J]. International Journal of Aviation, Aeronautics, and Aerospace, 2022, 9(4): 1756.