{"title":"航空疲劳和睡眠问题检测策略综述:人工智能的启示","authors":"Yan Li, Jibo He","doi":"10.1007/s11831-024-10123-5","DOIUrl":null,"url":null,"abstract":"<div><p>Over the past few years, the increasing occurrence of catastrophic accidents in aviation owing to human factors has raised several devastating threats to mankind. Recent progress in fatigue recognition among pilots made by Artificial intelligence (AI) has intensely begun to enhance the safety of the aviation sector by identifying and warning the potential catastrophic incidents caused by the impaired cognitive condition of aviation professionals. In this review, we have thoroughly investigated the implementation of AI-based approaches in the domain of aviation for fatigue detection. To the extent of our knowledge, it is clear that this review article is a new paper extremely devoted for investigating the advancements and challenges rendered by the AI-based approaches for addressing sleep and fatigue issues in aviation. Initially, we provided the basic definition of fatigue, various aspects provoking these problems among aviation professionals, and its effects in compromising aviation safety. Secondly, we illustrated a review of AI-based approaches developed for assessing fatigue and sleep problems in the context of aviation. Thirdly, the comparisons of various approaches are provided to summarize the efficiency of the existing works. Finally, we talked about the challenges encountered by the state-of-the-art approaches for identifying future research direction, and our suggested solutions are well presented for improving the efficiency of the fatigue detection approaches. This comprehensive research clearly depicts that the advancement of fatigue recognition approaches based on AI has a wider scope for mitigating pilot’s fatigue by identifying the mental state of the pilot earlier and providing adequate interventions.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4655 - 4672"},"PeriodicalIF":9.7000,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Review of Strategies to Detect Fatigue and Sleep Problems in Aviation: Insights from Artificial Intelligence\",\"authors\":\"Yan Li, Jibo He\",\"doi\":\"10.1007/s11831-024-10123-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Over the past few years, the increasing occurrence of catastrophic accidents in aviation owing to human factors has raised several devastating threats to mankind. Recent progress in fatigue recognition among pilots made by Artificial intelligence (AI) has intensely begun to enhance the safety of the aviation sector by identifying and warning the potential catastrophic incidents caused by the impaired cognitive condition of aviation professionals. In this review, we have thoroughly investigated the implementation of AI-based approaches in the domain of aviation for fatigue detection. To the extent of our knowledge, it is clear that this review article is a new paper extremely devoted for investigating the advancements and challenges rendered by the AI-based approaches for addressing sleep and fatigue issues in aviation. Initially, we provided the basic definition of fatigue, various aspects provoking these problems among aviation professionals, and its effects in compromising aviation safety. Secondly, we illustrated a review of AI-based approaches developed for assessing fatigue and sleep problems in the context of aviation. Thirdly, the comparisons of various approaches are provided to summarize the efficiency of the existing works. Finally, we talked about the challenges encountered by the state-of-the-art approaches for identifying future research direction, and our suggested solutions are well presented for improving the efficiency of the fatigue detection approaches. This comprehensive research clearly depicts that the advancement of fatigue recognition approaches based on AI has a wider scope for mitigating pilot’s fatigue by identifying the mental state of the pilot earlier and providing adequate interventions.</p></div>\",\"PeriodicalId\":55473,\"journal\":{\"name\":\"Archives of Computational Methods in Engineering\",\"volume\":\"31 8\",\"pages\":\"4655 - 4672\"},\"PeriodicalIF\":9.7000,\"publicationDate\":\"2024-04-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Archives of Computational Methods in Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s11831-024-10123-5\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archives of Computational Methods in Engineering","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s11831-024-10123-5","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
A Review of Strategies to Detect Fatigue and Sleep Problems in Aviation: Insights from Artificial Intelligence
Over the past few years, the increasing occurrence of catastrophic accidents in aviation owing to human factors has raised several devastating threats to mankind. Recent progress in fatigue recognition among pilots made by Artificial intelligence (AI) has intensely begun to enhance the safety of the aviation sector by identifying and warning the potential catastrophic incidents caused by the impaired cognitive condition of aviation professionals. In this review, we have thoroughly investigated the implementation of AI-based approaches in the domain of aviation for fatigue detection. To the extent of our knowledge, it is clear that this review article is a new paper extremely devoted for investigating the advancements and challenges rendered by the AI-based approaches for addressing sleep and fatigue issues in aviation. Initially, we provided the basic definition of fatigue, various aspects provoking these problems among aviation professionals, and its effects in compromising aviation safety. Secondly, we illustrated a review of AI-based approaches developed for assessing fatigue and sleep problems in the context of aviation. Thirdly, the comparisons of various approaches are provided to summarize the efficiency of the existing works. Finally, we talked about the challenges encountered by the state-of-the-art approaches for identifying future research direction, and our suggested solutions are well presented for improving the efficiency of the fatigue detection approaches. This comprehensive research clearly depicts that the advancement of fatigue recognition approaches based on AI has a wider scope for mitigating pilot’s fatigue by identifying the mental state of the pilot earlier and providing adequate interventions.
期刊介绍:
Archives of Computational Methods in Engineering
Aim and Scope:
Archives of Computational Methods in Engineering serves as an active forum for disseminating research and advanced practices in computational engineering, particularly focusing on mechanics and related fields. The journal emphasizes extended state-of-the-art reviews in selected areas, a unique feature of its publication.
Review Format:
Reviews published in the journal offer:
A survey of current literature
Critical exposition of topics in their full complexity
By organizing the information in this manner, readers can quickly grasp the focus, coverage, and unique features of the Archives of Computational Methods in Engineering.