Systematic review of cognitive impairment in drivers through mental workload using physiological measures of heart rate variability.

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-10-31 eCollection Date: 2024-01-01 DOI:10.3389/fncom.2024.1475530
Mansoor S Raza, Mohsin Murtaza, Chi-Tsun Cheng, Muhana M A Muslam, Bader M Albahlal
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Abstract

The intricate interplay between driver cognitive dysfunction, mental workload (MWL), and heart rate variability (HRV) provides a captivating avenue for investigation within the domain of transportation safety studies. This article provides a systematic review and examines cognitive hindrance stemming from mental workload and heart rate variability. It scrutinizes the mental workload experienced by drivers by leveraging data gleaned from prior studies that employed heart rate monitoring systems and eye tracking technology, thereby illuminating the correlation between cognitive impairment, mental workload, and physiological indicators such as heart rate and ocular movements. The investigation is grounded in the premise that the mental workload of drivers can be assessed through physiological cues, such as heart rate and eye movements. The study discovered that HRV and infrared (IR) measurements played a crucial role in evaluating fatigue and workload for skilled drivers. However, the study overlooked potential factors contributing to cognitive impairment in drivers and could benefit from incorporating alternative indicators of cognitive workload for deeper insights. Furthermore, investigated driving simulators demonstrated that an eco-safe driving Human-Machine Interface (HMI) significantly promoted safe driving behaviors without imposing excessive mental and visual workload on drivers. Recommendations were made for future studies to consider additional indicators of cognitive workload, such as subjective assessments or task performance metrics, for a more comprehensive understanding.

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利用心率变异性的生理测量方法,通过精神工作量对驾驶员认知障碍进行系统审查。
驾驶员认知功能障碍、脑力劳动负荷(MWL)和心率变异性(HRV)之间错综复杂的相互作用,为交通安全研究领域提供了一个引人入胜的调查途径。本文对精神工作量和心率变异性引起的认知障碍进行了系统回顾和研究。文章利用之前采用心率监测系统和眼动跟踪技术进行的研究中收集的数据,仔细研究了驾驶员所经历的脑力劳动负荷,从而揭示了认知障碍、脑力劳动负荷以及心率和眼动等生理指标之间的相关性。调查的前提是,可以通过心率和眼球运动等生理线索来评估驾驶员的脑力劳动负荷。研究发现,心率变异和红外线(IR)测量在评估熟练驾驶员的疲劳和工作量方面起着至关重要的作用。然而,该研究忽略了导致驾驶员认知障碍的潜在因素,如果能纳入认知工作量的替代指标,将有助于获得更深入的见解。此外,对驾驶模拟器的调查表明,生态安全驾驶人机界面(HMI)可显著促进安全驾驶行为,而不会给驾驶员带来过多的精神和视觉工作量。建议今后的研究考虑认知工作量的其他指标,如主观评估或任务绩效指标,以便更全面地了解情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
期刊最新文献
Editorial: Computational modeling and machine learning methods in neurodevelopment and neurodegeneration: from basic research to clinical applications. Simulated synapse loss induces depression-like behaviors in deep reinforcement learning. Systematic review of cognitive impairment in drivers through mental workload using physiological measures of heart rate variability. Facial emotion recognition using deep quantum and advanced transfer learning mechanism. BrainNet: an automated approach for brain stress prediction utilizing electrodermal activity signal with XLNet model.
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