The Relation between Mental Workload and Face Temperature in Flight Simulation

In this research, we study the relationship between mental workload and facial temperature of aircraft participants during a simulated takeoff flight. We conducted experiments to comprehend the correlation between work and facial temperature within the flight simulator. The experiment involved a group of 10 participants who played the role of pilots in a simulated A-320 flight. Six different flying scenarios were designed to simulate normal and emergency situations on airplane takeoff that would occur in different levels of mental workload for the participants. The measurements were workload assessment, face temperatures, and heart rate monitoring. Throughout the experiments, we collected a total of 120 instances of takeoffs, together with over 10 hours of time-series data including heart rate, workload, and face thermal images and temperatures. Comparative analysis of EEG data and thermal image types, revealed intriguing findings. The results indicate a notable inverse relationship between workload and facial muscle temperatures, as well as facial landmark points. The results of this study contribute to a deeper understanding of the physiological effects of workload, as well as practical implications for aviation safety and performance.

DISCRIMINATION OF MENTAL WORKLOAD LEVELS IN HUMAN SUBJECTS WITH FUNCTIONAL NEAR-INFRARED SPECTROSCOPY

We have applied functional near-infrared spectroscopy(fNIRS)to the human forehead to distinguish different levels of mental workload on the basis of hemodynamic changes occurring in the prefrontal cortex.We report data on 3 subjects from a protocol involving 3 mental workload levels based on to working memory tasks.To quantify the potential of fNIRS for mental workload discrimination,we have applied a 3-nearest neighbor classification algorithm based on the amplitude of oxyhemoglobin(HbO2)and deoxyhemoglobin(HbR)concentration changes associated with the working memory tasks.We have found classification success rates in the range of 44%-72%,which are significantly higher than the corresponding chance level(for random data)of 19.1%.This work shows the potential of fNIRS for mental workload classification,especially when more parameters(rather than just the amplitude of concentration changes used here)and more sophisticated classification algorithms(rather than the simple 3-nearest neighbor algorithm used here)are considered and optimized for this application.

Measuring Mental Workload Using ERPs Based on FIR,ICA,and MARA

Mental workload is considered to be strongly linked to human performance,and the ability to measure it accurately is key for balancing human health and work.In this study,brain signals were elicited by mental arithmetic tasks of varying difficulty to stimulate different levels of mental workload.In addition,a finite impulse response(FIR)filter,independent component analysis(ICA),and multiple artifact rejection algorithms(MARAs)were used to filter event-related potentials(ERPs).Then,the data consisting of ERPs,subjective ratings of mental workload,and task performance,were analyzed through the use of variance and Spearman’s correlation during a simulated computer task.We found that participants responded faster and performed better in the easy task condition,followed by the medium and high-difficulty conditions,which verifies the validity of the ERP filtering.Moreover,larger P2 and P3 waveforms were evoked as the task difficulty increased,and a higher task difficulty elicited a more enhanced N300.Correlation analysis revealed a negative relationship between the amplitude of P3 and the subjective ratings,and a positive relationship between the P3 amplitude and accuracy.The results presented in this paper demonstrate that a combination of FIR,ICA,and MARA methods can filter ERPs in the non-invasive real-time measurement of workload.Additionally,frontocentral P2,N3,and parietal P3 components showed differences between genders.The proposed measurement of mental workload can be useful for real-time identification of mental states and can be applied to human-computer interaction in the future.

Measurement and Evaluation of Pilot Mental Workload Based on Flight Simulation System

The digital simulation technology is adopted to establish the flight environment and scenario,which realizes the flight display instrument interface and unusual attitude recovery task.The humancomputer interaction(HCI) experiment is carried out in the flight simulation system,and the behavior performances and physiological parameters are recorded as the evaluation indices of mental workload.Participants are required to perform the unusual attitude recovery tasks and keep monitoring the target flight indicators.Once the unusual attitude occurs,the corresponding operation of steering wheel is expected to be taken immediately to response and eliminate it.The results show that most of behavior performances and physiological parameters are sensitive to mental workload and some of the indices are significantly correlative.

Comprehensive Assessment of Pilot Mental Workload in Various Levels

Physiological measures indexed by fixation frequency and total fixation time,task performance based on n-back accuracy,subjective assessment based on NASA Task Load Index(NASA-TLX) were used to measure the Mental Workload(MW) in different levels which were induced by vision-related flight task combined with auditory cognitive load. 16 healthy novice pilots were recruited to complete a monitoring task based on Head-Up Display(HUD) and an auditory n-back task which was used to manipulate the Mental Workload Level(MWL) in flight simulation environment. In our experiment,fixation frequency,average saccade time,blink rate and average pupil diameter were sensitive to MW. What's more,a comprehensive assessment method of pilot mental workload based on various measures was advocated. At last,a Fisher projection function based on the Fisher discrimination method and a three-level discriminate model established by the Bayes discrimination method were built up,the original validation and cross-validation methods of both models were 97. 92% and 95. 83% respectively,which could discriminate various Mental Workload Levels(MWLs) ideally.

Ergonomic Evaluation of the Perceived Mental Workload among Tunisian University Hospital Caregivers

Objectives: To evaluate the perceived mental workload and to identify its individual and professional determinants among Tunisian caregivers in university hospitals. Methods: A cross-sectional study was carried out in two university hospitals of the center of Tunisia, which employ 1179 paramedics providing care activities. The survey was conducted within a representative sample, of 301 caregivers assigned to 28 medical and surgical services, randomly drawn, after being paired according to age, gender and work schedule. It included structured inquiry (socio-professional characteristics, occupational perceived workload) and validated questionnaires (Job Content Questionnaire of Karasek, SF12, WAI). Results: Mental workload was perceived as “moderate” by 145 caregivers (48.49%) and as “heavy” by 119 caregivers (40.61% of them). Among the sample, 209 paramedics endured at least once the fixed night work schedules along their respective careers, with an average of 5.63 ± 7.54 years. According to the WAI index, 39.59% of subjects considered their capacity of work as low. Moreover, job strain was noted in 19.79%. Indeed, work latitude was unsatisfactory among 41% of caregiver, 60.1% of them mentioned the absence of colleagues’ support. A feeling of job insecurity was reported by 73.72% of subjects. Multi-varied analytic study concluded that heavier perceived mental work load was correlated to individual determinants particularly age >45 years (p -3), overweight or obesity (p = 0.02), more than three depending children (p -3), lower degree obtained (p -3), working grade as nurse assistant (P = 0.001), lower quality of physical life (0.010) and expressed willingness to retire early (p = 0.003). Conclusion: The heavy perception of mental workload associated to care activities, is one of the reasons of premature departure among nurses from professional activity. Preventive actions on professional determinants of perceived workload can reduce this phenomenon.

A model for discrimination and prediction of mental workload of aircraft cockpit display interface

With respect to the ergonomic evaluation and optimization in the mental task design of the aircraft cockpit display interface, the experimental measurement and theoretical modeling of mental workload were carried out under flight simulation task conditions using the performance evaluation, subjective evaluation and physiological measurement methods. The experimental results show that with an increased mental workload, the detection accuracy of flight operation significantly reduced and the reaction time was significantly prolonged; the standard deviation of R-R intervals（SDNN） significantly decreased, while the mean heart rate exhibited little change; the score of NASA_TLX scale significantly increased. On this basis, the indexes sensitive to mental workload were screened, and an integrated model for the discrimination and prediction of mental workload of aircraft cockpit display interface was established based on the Bayesian Fisher discrimination and classification method. The original validation and cross-validation methods were employed to test the accuracy of the results of discrimination and prediction of the integrated model, and the average prediction accuracies determined by these two methods are both higher than 85%. Meanwhile, the integrated model shows a higher accuracy in discrimination and prediction of mental workload compared with single indexes. The model proposed in this paper exhibits a satisfactory coincidence with the measured data and could accurately reflect the variation characteristics of the mental workload of aircraft cockpit display interface, thus providing a basis for the ergonomic evaluation and optimization design of the aircraft cockpit display interface in the future.

Detection and Recuperation of Mental Fatigue

Mental fatigue is a complex state that results from prolonged cognitive activity. Symptoms of mental fatigue can include change in mood, motivation, and temporary deterioration of various cognitive functions involved in goal-directed behavior. Extensive research has been done to develop methods for recognizing physiological and psychophysiological signs of mental fatigue. This has allowed the development of many AI-based models to classify different levels of fatigue, using data extracted from eye-tracking device, EEG, or ECG. In this paper, we present an experimental protocol which aims to both generate/measure mental fatigue and provide effective strategies for recuperation via VR sessions paired with EEG and eye tracking devices. This paper first provides a comprehensive state-of-the-art of mental fatigue predictive factors, measurement methods, and recuperation strategies. Then the paper presents an experimental protocol resulting from the state-of-the-art to 1) generate and measure mental fatigue and 2) evaluate the effectiveness of virtual therapy for fatigue recuperation, using a virtual reality (VR) simulated environment. In our work, we successfully generated mental fatigue through completion of cognitive tasks in a virtual simulated environment. Participants showed significant decline in pupil diameter and theta/alpha score during the various cognitive tasks. We trained an RBF SVM classifier from Electroencephalogram (EEG) data classifying mental fatigue with 95% accuracy on the test set. Finally, our results show that the time allocated for virtual therapy did not improve pupil diameter in post-relaxation period. Further research on the impact of relaxation therapy on relaxation therapy should allocate time closer to the standard recovery time of 60 min.

Evaluating the moderating effect of in-vehicle warning information on mental workload and collision avoidance performance

Purpose–The presentation of in-vehicle warnings information at risky driving scenarios is aimed to improve the collision avoidance ability of drivers.Existing studies have found that driver’s collision avoidance performance is affected by both warning information and driver’s workload.However,whether moderation and mediation effects exist among warning information,driver’s cognition,behavior and risky avoidance performance is unclear.Design/methodology/approach–This purpose of this study is to examine whether the warning information type modifies the relationship between the forward collision risk and collision avoidance behavior.A driving simulator experiment was conducted with waring and command information.Findings–Results of 30 participants indicated that command information improves collision avoidance behavior more than notification warning under the forward collision risky driving scenario.The primary reason for this is that collision avoidance behavior can be negatively affected by the forward collision risk.At the same time,command information can weaken this negative effect.Moreover,improved collision avoidance behavior can be achieved through increasing drivers’mental workload.Practical implications–The proposed model provides a comprehensive understanding of the factors influencing collision avoidance behavior,thus contributing to improved in-vehicle information system design.Originality/value–The significant moderation effects evoke the fact that information types and mental workloads are critical in improving drivers’collision avoidance ability.Through further calibration with larger sample size,the proposed structural model can be used to predict the effect of invehicle warnings in different risky driving scenarios.

多生理信号融合的飞行员脑力负荷分类研究

为了解决飞行员脑力负荷状态分类的问题,采用了结合多头注意力机制的神经网络模型,将脑电信号、心电信号和肌电信号进行多模态数据融合,得到了高效的飞行员脑力负荷分类决策模型。研究结果证实了融合多生物电信号在该脑力负荷状态分类方法中的有效性,准确率可达到96.54%,比前人关于单生理信号研究的准确率提高了10.11%,比前人融合多生理信号决策研究准确率提高了2.32%,这为推进航空安全事业的研究提供了有益的参考。

领悟社会支持在护士应急能力与心理负荷的中介效应分析

目的 探讨领悟社会支持在护士应急能力与心理负荷间的中介效应。方法 采用便利抽样法,于2022年5—12月选取成都市5所综合医院的605名护士作为调查对象;采用NASA任务负荷指数量表、护士突发性公共卫生事件应急能力量表和领悟社会支持量表进行调查,构建并检验中介模型。结果 本组护士心理负荷所有条目均分为(57.41±14.50)分,应急能力总分为(64.59±13.49)分,领悟社会支持总分为(58.60±15.77)分。护士心理负荷条目均分与应急能力总分(r=-0.356)和领悟社会支持总分(r=-0.341)均呈负相关(均P<0.01)。领悟社会支持在护士应急能力与心理负荷间起部分中介作用,中介效应值为-0.139,效应占比为26.8%。结论 本组护士心理负荷、应急能力和领悟社会支持均处于中等水平,领悟社会支持在护士应急能力与心理负荷之间起部分中介作用。建议护理管理者及家庭从多方面予以护士支持,提高护士的应急能力和领悟社会支持水平,进而降低其心理负荷。

面向复杂装配任务的脑力负荷综合评估方法

复杂装配作业脑力负荷过高会降低装配绩效和质量,甚至引发安全事故。因此,对装配任务脑力负荷进行准确测量与评估,确定作业人员的脑力负荷水平,是生产系统优化的重要依据。本文针对复杂装配任务的脑力负荷评估问题,设计了基于Lego积木模拟的装配任务实验,使用主观测量法、绩效测量法和生理测量法采集了27项指标数据,分析了各项数据对脑力负荷变化的敏感性,发现绩效测量指标、主观测量指标、注视次数、总注视时间、扫视次数、总扫视时间和平均瞳孔直径这7项指标是复杂装配任务脑力负荷的有效测量指标。基于有效测量指标,采用BP(back propagation)神经网络和贝叶斯线性判别两种建模方法,构建了装配任务脑力负荷综合评估模型。研究结果表明,使用因子分析将7维指标转化为二维综合指标作为主成分输入,使用归一化共轭梯度法为训练算法的BP神经网络模型是装配作业脑力负荷的最佳综合评估模型,其判别准确率达到84.80%。本文提出的脑力负荷测量与评估方法可为装配作业脑力负荷评定和优化提供参考依据。

基于心率变异性的飞行员脑力负荷特征

为了探索五边飞行不同阶段飞行员脑力负荷水平,寻找表征脑力负荷的心电指标,对飞行学员展开五边飞行任务的六自由度模拟飞行实验,测量飞行过程中的脑力负荷水平,采集不同飞行阶段飞行员心电各指标数据,利用因子分析构建飞行员脑力负荷模型,分析和验证不同飞行阶段飞行员脑力负荷的变化特征。结果表明:相比心率指标,心率变异性指标在测量飞行员脑力负荷时具有更高的信度和效度;起飞和上升阶段的脑力负荷处于较低水平并呈下降趋势;巡航阶段的脑力负荷水平较低且变化趋势平滑;进近着陆阶段的脑力负荷水平最高。此外,突发事件会显著影响脑力负荷,引起飞行员脑力负荷的显著增加。

实际任务背景下脑力负荷的实时监测与评估研究现状

脑力负荷是影响飞行员等关键岗位职业安全的重要风险因素之一,而任务中实时监测作业者的脑力负荷,对于及时识别疲劳状态及预防人因失误具有重大意义。从脑力工作负荷实时评估技术、任务中生物学信号获取方法、脑力任务设置方法与脑力状态识别模型构建等四个方面,本文分别介绍了实际工作场景脑力负荷监测的关键环节的研究现状、存在问题及对策;在此基础上,总结了实际工作场景中脑力负荷研究的重点:便携式生物信号采集技术的研发、考虑任务中不同生理模式与认知交互作用影响、多层次不同模态数据融合的脑力负荷评价模型。本文可以为实际动态脑力工作场景下任务负荷实时监测提供新思路,为疲劳状态的精准识别与事故预防提供充实的理论基础。

基于混合自编码器的脑力负荷识别

为了消除多通道近红外光谱信号中存在的冗余信息并提取抽象特征,构建了基于混合自编码器的脑力负荷识别模型。首先,将原始信号送入栈式自编码器中进行通道降维;然后使用卷积自编码器对降维后的信号进行无监督学习从而提取抽象特征,并将特征依次送入支持向量机、K最近邻、随机森林这3种基分类器中进行建模;最后,用软、硬投票的集成策略来提高模型对脑力负荷识别的准确性。实验结果表明,混合自编码器具有良好的通道降维和提取抽象特征的能力,该模型在脑力负荷三分类任务中的准确率可以达到95.12%,相对于同类研究准确率有明显提升。

基于样本选择的跨被试脑力负荷识别

人体的脑力负荷状态与人机操作工作时的工作效率、人力资源分配以及事故的发生等息息相关,因此研究操作人员的脑力负荷状态具有重要意义。为了解决现有脑力负荷识别方法由于训练集中样本数量过少导致分类效果较差的问题,提出了一种基于样本选择的跨被试脑力负荷识别方法。首先,将其他被试的脑电数据作为训练集,参考目标被试的少量历史数据对训练集中的特征数据进行样本选择,实现减少样本数量的同时减少训练集和测试集之间的域差异;其次,再通过主成分分析对样本选择后的自适应训练集和目标被试测试集特征进行特征降维;最后,用自适应训练集主成分建立支持向量机分类模型识别测试集样本的脑力负荷状态。结果表明,该方法可以在提高分类效率的同时提高分类精度,实现快速、准确的脑力负荷状态识别。

急诊科护士应对重大突发公共卫生事件心理负荷现状及潜在剖面分析

目的调查急诊科护士应对重大突发公共卫生事件过程中的心理负荷现状,进行潜在剖面分析并探讨其影响因素。方法2022年12月-2023年1月,采用便利抽样法选取浙江省9所综合性医院(三级医院5所,二级医院4所)的急诊科护士进行调查。调查工具采用自制一般资料调查问卷、NASA任务负荷指数量表、心理资本调查问卷、一般健康问卷。结果共发放466份问卷,回收456份,有效率97.85%。456名急诊科护士在应对重大突发公共卫生事件过程心理负荷总分为(91.25±11.75)分,心理资本调查问卷总分为(100.08±13.89)分,一般健康问卷总分为(2.34±2.90)分。潜在剖面分析将其分为4个类别,根据类别特征命名为C1低负荷感受-高自我评价(10.1%)、C2高负荷感受-高自我评价(24.1%)、C3中负荷感受-中自我评价(37.2%)、C4高负荷感受-低自我评价(28.5%)。有序多分类Logistic回归显示,年龄、工作年限、文化程度、医院等级、心理健康、心理资本是急诊科护士在应对重大突发公共卫生事件心理负荷的影响因素。结论急诊科护士在应对重大突发公共卫生事件过程中心理负荷处于高水平,其中高负荷感受-低自我评价人群的心理健康状况处于高危状态,应引起重点关注,心理资本与心理负荷呈负相关,提示护理管理者制订针对性的干预措施,通过提高心理资本来达到缓解急诊科护士应对重大公共卫生事件过程中的高心理负荷目的。

执飞任务中剩余脑力负荷量化评估模型

为评估飞行员在执飞任务中的脑力负荷,建立了基于任务需求负荷(TDL)和人员负荷能力(SWC)的脑力负荷量化评估模型,并据此提出剩余脑力负荷(SMWL)和脑力负荷占用率(ORMWL)的评估方法。采用信息熵法对信息显控界面上的信息量进行量化。基于多属性任务组(MATB)平台设计的执飞情境实验,对15名被试人员在不同任务水平下的脑力负荷状态进行评估,以验证所建模型的有效性。结果表明:SMWL模型方法的脑力负荷值与NASA-TLX主观量表值存在显著相关性。此外,任务水平的增加对人员的脑力负荷值有显著的U型影响。因此,通过所建模型,基于SMWL和ORMWL的概念,可以对飞行员执行飞行任务时的脑力负荷进行实时、定量的评估,为脑力负荷的量化提供了一种新的思路。

基于任务情境的航线飞行员脑力负荷特征研究

为探究飞行任务中航线飞行员的脑力负荷特征,开展了2种经验水平(机长、副驾驶)×3种任务阶段(进近阶段、火警阶段、着陆阶段)的混合双因素设计试验,记录HR、HRV、EDA和眼动指标作为因变量,建立广义线性混合模型并分析数据。结果显示,与副驾驶组相比,机长组HR均值、SCL、SCR和平均瞳孔直径更小,IBI均值和PNN20更大;被试在不同任务阶段的脑力负荷生理特征亦存在差异。研究表明,航线飞行员的脑力负荷特征受到经验水平的影响,机长在全部任务阶段中脑力负荷均低于副驾驶;不同任务阶段的脑力负荷特征存在差异,由进近阶段到着陆阶段,航线飞行员的脑力负荷表现出逐级上升的趋势。研究结果可为实现循证训练、提高训练效率提供数据支持,有助于提升飞行安全水平。

基于图卷积神经网络的脑力负荷识别

针对生理信号特征提取过程依赖于先验知识,且传统深度学习算法不考虑通道间耦合关系的问题,构建了基于图卷积神经网络的脑力负荷识别模型。数据集包含多通道连续采集的近红外光谱,分别计算基于互信息、锁相值和皮尔森相关系数的连通性矩阵来反映通道间的内在联系,并将近红外光谱和连通性矩阵组成图结构输入到图卷积神经网络。实验结果表明,该模型具有良好的抽象特征提取能力,在输入中融合通道间相关性系数有助于提升脑力负荷的识别精度,且连通性矩阵的可视化结果表明大脑额叶区对脑力负荷变化较敏感。