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.

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.

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.

The Influence of a User-Centred Design Focus on the Effectiveness of a User Interface for an Agricultural Machine

As agricultural machines become more complex, it is increasingly critical that special attention be directed to the design of the user interface to ensure that the operator will have an adequate understanding of the status of the machine at all times. A user-centred design focus was employed to develop two conceptual designs (UCD1 & UCD2) for a user interface for an agricultural air seeder. The two concepts were compared against an existing user interface (baseline condition) using the metrics of situation awareness (Situation Awareness Global Assessment Technique), mental workload (Integrated Workload Scale), reaction time, and subjective feedback. There were no statistically significant differences among the three user interfaces based on the metric of situation awareness;however, UCD2 was deemed to be significantly better than either UCD1 or the baseline interface on the basis of mental workload, reaction time and subjective feedback. The research has demonstrated that a user-centred design focus will generate a better user interface for an agricultural machine.

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.

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.