[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120865-en":3,"doc-seo-120865-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120865,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Advancing Aviation Safety Through Machine Learning and Psychophysiological Data - A Systematic Review","Aviation safety remains essential in an industry where pilot errors are frequently linked to workload, fatigue, stress, and emotional disturbances. A systematic literature review examines how psychophysiological data combined with machine learning can improve safety by modeling pilot behavior. Using a widely accepted methodology, the review analyzes 80 peer-reviewed studies from 3352 records across five databases. It details behavioral focus, data types, preprocessing, model families, and evaluation metrics, highlighting research concentration on workload and fatigue, limited use of deep learning, and key gaps in preprocessing effects, data-environment diversity, and explainability.","Received 8 November 2023, accepted 27 December 2023, date of publication 3 January 2024, date of current version 11 January 2024.  \nDigital Object Identifier 10.1109/ACCESS.2024.3349495  \nAdvancing Aviation Safety Through Machine Learning and Psychophysiological Data:  \nA Systematic Review  \nIBRAHIM ALRESHIDI1,2,3, IRENE MOULITSAS1,2, AND KARL W. JENKINS 1  \n1Centre for Computational Engineering Sciences, Cranfield University, MK43 0AL Cranfield, U.K.  \n2Machine Learning and Data Analytics Laboratory, Digital Aviation Research and Technology Centre (DARTeC), Cranfield University, MK43 0AL Cranfield, U.K.  \n3College of Computer Science and Engineering, University of Ha’il, Hail 81451, Saudi Arabia  \nCorresponding authors: Ibrahim Alreshidi (ibrahim.alreshidi@cranfield.ac.uk) and Irene Moulitsas (i.moulitsas@cranfield.ac.uk)  \nABSTRACT In the aviation industry, safety remains vital, often compromised by pilot errors attributed to factors such as workload, fatigue, stress, and emotional disturbances. To address these challenges, recent research has increasingly leveraged psychophysiological data and machine learning techniques, offering the potential to enhance safety by understanding pilot behavior. This systematic literature review rigorously follows a widely accepted methodology, scrutinizing 80 peer-reviewed studies out of 3352 studies from five key electronic databases. The paper focuses on behavioral aspects, data types, preprocessing techniques, machine learning models, and performance metrics used in existing studies. It reveals that the majority of research disproportionately concentrates on workload and fatigue, leaving behavioral aspects like emotional responses and attention dynamics less explored. Machine learning models such as tree-based and support vector machines are most commonly employed, but the utilization of advanced techniques like deep learning remains limited. Traditional preprocessing techniques dominate the landscape, urging the need for advanced methods. Data imbalance and its impact on model performance is identified as a critical, underresearched area. The review uncovers significant methodological gaps, including the unexplored influence of preprocessing on model efficacy, lack of diversification in data collection environments, and limited focus on model explainability. The paper concludes by advocating for targeted future research to address these gaps, thereby promoting both methodological innovation and a more comprehensive understanding of pilot behavior.  \nINDEX TERMS Aviation safety, machine learning, deep learning, mental states classification, pilot behavior, systematic review, psychophysiological signals, EEG.  \nI. INTRODUCTION  \nAs the global aviation industry undergoes transformative technological advancements, the role of pilots is concurrently evolving from simply operating machinery to making critical decisions in high-stakes, dynamic environments [1] . In light of the complex nature of contemporary aviation operations, a comprehensive understanding of pilot behavior becomes paramount for enhancing aviation safety. Machine  \nThe associate editor coordinating the review of this manuscript and  \napproving it for publication was Gang Wang  .  \nLearning (ML) technologies, particularly when integrated with psychophysiological data such as electroencephalogram (EEG), present a promising route for in-depth investigation into this vital area. These cutting-edge methodologies enable researchers to acquire nuanced insights into various facets of pilot behavior, including cognitive states and emotional responses. This paper serves as a systematic literature review, conducted in accordance with the rigorous methodological guidelines [2],[3],[4] . It aims to offer an exhaustive synthesis of existing research on the application of ML techniques and psychophysiological data for understanding pilot behavior.  \n􀀊 2024 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License.","cbCaikVBRrGiFEys","https://ap.wps.com/l/cbCaikVBRrGiFEys","pdf",4873440,1,20,"English","en",105,"# Abstract\n# Introduction\n## Importance of Aviation Safety","[{\"question\":\"What problem does the review address in aviation safety research?\",\"answer\":\"The review targets how aviation safety can be improved when pilot errors are associated with workload, fatigue, stress, and emotional disturbances through psychophysiological data and machine learning.\"},{\"question\":\"How was the systematic literature review conducted?\",\"answer\":\"It follows a widely accepted systematic review methodology and analyzes 80 peer-reviewed studies selected from 3352 studies across five key electronic databases.\"},{\"question\":\"What gaps and limitations does the review identify?\",\"answer\":\"It finds disproportionate focus on workload and fatigue, limited exploration of behavioral factors like emotions and attention, constrained deep-learning adoption, underresearch on data imbalance, and insufficient study of preprocessing influence and model explainability.\"}]","Advancing Aviation Safety Through Machine Learning and Psychophysiological Data - 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