A human-system integration framework and its application for special vehicle interface design under typical human readiness levels

Life cycle Human System Integration (HSI) practices are crucial for optimizing human system performance, reducing costs, and ensuring safety. To address the limited HSI practices under typical Human Readiness Levels (HRLs), our study proposes an HSI theoretical framework and applies it to the design of human-machine interfaces (HMIs) for special vehicles. A stakeholder survey evaluates effectiveness of the framework and its application. Conclusions: (1) The framework, based on the input-process-output model, covers HSI processes and their support across HRLs. (2) The case study of HMI design in HRLs 4-6 identifies key processes and their specific support, contributing to the refinement of the framework. (3) The stakeholder survey underscores the importance and effectiveness of HSI processes and their support in the case study for life cycle human factor practices, suggesting areas for improvement in structuring and operability. The study offers insights into HSI practices under typical HRLs, merging theoretical and case study perspectives.

Influencing factors of novice pilot SA based on DEMATEL-AISM method: From pilots' view

Pilot situation awareness (SA) regulates flight safety, and inexperience may impair novice pilot reliability in SA. This study aims to determine the key influencing factors of novice pilot SA and to analyze the interrelationship and interaction mechanism of the factors. We investigated 55 novice pilots trained at aviation schools and identified the influencing factor index system by the Delphi survey. The method of Decision Making Trial and Evaluation (DEMATEL) combined with Adversarial Interpretive Structure Modeling (AISM) was adopted. The results show that: (1) The influencing factor index system includes 18 factors, divided into four categories: individual factors, team factors, task and human-machine system factors, and cockpit environment factors. (2) Team communication, team cooperation, basic cognitive ability, interface design, occupational age and experience, and authority gradient are the six crucial influencing factors. The former three have the greatest association with other factors, while the latter three are most likely to affect other factors. (3) Team communication, basic cognitive ability, and interface design are root-cause factors, of which team communication is the most fundamental. (4) The results of DEMATEL and AISM are consistent, both disclosing team communication as the fundamental factor with the highest priority, and cockpit environmental factors as the direct influencing factors but most susceptible to other factors. The present study can be viewed as a conducive attempt to extract vital influencing factors of novice pilot SA, and to provide ergonomic insights for determining the priorities to improve novice pilot SA in training and aircraft design for flight safety.

Pilots’ mental workload prediction based on timeline analysis

BACKGROUND:

The aircraft cockpit is a highly intensive human-computer interaction system, and its design directly affects flight safety.

OBJECTIVE:

To optimize the display interface design in complex flight tasks, the present study aimed to propose a dynamic conceptual framework and a timeline task analysis method for the quantization of the dynamic time effect of mental workload and the influencing factors of task types in the mental workload prediction model.

METHODS:

The multi-factor mental workload prediction model based on attention resource allocation was integrated to establish the dynamic prediction model of mental workload. The ergonomics simulation experiment was carried out by recording the data on the performance of embedded subtasks, National Aeronautics and Space Administration-Task Load Index (NASA-TLX) subjective evaluation, and eye tracking.

RESULTS:

The results indicated that the prediction model had a good prediction accuracy and effectiveness under different simulated interfaces and complex tasks, and the real-time monitoring of pilots’ mental workload state was realized.

CONCLUSION:

In conclusion, the prediction model and the experimental method could be applied to avoid the overload of the pilot throughout the flight phase by optimizing the display interface and adjusting the flight task.

A comprehensive prediction and evaluation method of pilot workload

BACKGROUND:

The prediction and evaluation of pilot workload is a key problem in human factor airworthiness of cockpit.

OBJECTIVE:

A pilot traffic pattern task was designed in a flight simulation environment in order to carry out the pilot workload prediction and improve the evaluation method.

METHODS:

The prediction of typical flight subtasks and dynamic workloads (cruise, approach, and landing) were built up based on multiple resource theory, and a favorable validity was achieved by the correlation analysis verification between sensitive physiological data and the predicted value.

RESULTS:

Statistical analysis indicated that eye movement indices (fixation frequency, mean fixation time, saccade frequency, mean saccade time, and mean pupil diameter), Electrocardiogram indices (mean normal-to-normal interval and the ratio between low frequency and sum of low frequency and high frequency), and Electrodermal Activity indices (mean tonic and mean phasic) were all sensitive to typical workloads of subjects.

CONCLUSION:

A multinominal logistic regression model based on combination of physiological indices (fixation frequency, mean normal-to-normal interval, the ratio between low frequency and sum of low frequency and high frequency, and mean tonic) was constructed, and the discriminate accuracy was comparatively ideal with a rate of 84.85%.

Pilot's visual attention allocation modeling under fatigue

BACKGROUND

Human factors involved with visual attention mechanism and fatigue are critical causes of modern aviation accidents.

OBJECTIVE

To investigate the connection between attention and flight fatigue, a mathematical model of pilot's visual attention allocation was established based on information processing channels. Multi-task condition and current psychophysical state were taken into account as well.

METHODS

Sixteen participants were recruited to perform a long-term dual-task in a Boeing 737-800 flight simulator. The primary task was an envelope flight task and the secondary was an unusual attitude (UA) recovery task. Reaction time of the secondary task was recorded as a behavior performance index, while heart rate and respiration rate were measured as physiological indices as well as fixation distribution as attention allocation index.

RESULTS

The experiment results showed a significant affect of experiment time that indicated the occurrence and influence of fatigue. Eye movement tracking also revealed good agreement with the predictable model and hence verified its effectiveness. Moreover, applicability of the model was validated under flight fatigue and multiple tasks condition.

CONCLUSION

The current study provided a quantitative connection between pilot's visual attention allocation and flight fatigue, which was verified in the ergonomics experiment.

Mental workload prediction based on attentional resource allocation and information processing

Mental workload is an important component in complex human-machine systems. The limited applicability of empirical workload measures produces the need for workload modeling and prediction methods. In the present study, a mental workload prediction model is built on the basis of attentional resource allocation and information processing to ensure pilots' accuracy and speed in understanding large amounts of flight information on the cockpit display interface. Validation with an empirical study of an abnormal attitude recovery task showed that this model's prediction of mental workload highly correlated with experimental results. This mental workload prediction model provides a new tool for optimizing human factors interface design and reducing human errors.

Improving pilot mental workload evaluation with combined measures

Behavioral performance, subjective assessment based on NASA Task Load Index (NASA-TLX), as well as physiological measures indexed by electrocardiograph (ECG), event-related potential (ERP), and eye tracking data were used to assess the mental workload (MW) related to flight tasks. Flight simulation tasks were carried out by 12 healthy participants under different MW conditions. The MW conditions were manipulated by setting the quantity of flight indicators presented on the head-up display (HUD) in the cruise phase. In this experiment, the behavioral performance and NASA-TLX could reflect the changes of MW ideally. For physiological measures, the indices of heart rate variability (HRV), P3a, pupil diameter and eyelid opening were verified to be sensitive to MW changes. Our findings can be applied to the comprehensive evaluation of MW during flight tasks and the further quantitative classification.

Modeling the situation awareness by the analysis of cognitive process

To predict changes of situation awareness (SA) for pilot operating with different display interfaces and tasks, a qualitative analysis and quantitative calculation joint SA model was proposed. Based on the situational awareness model according to the attention allocation built previously, the pilot cognitive process for the situation elements was analyzed according to the ACT-R (Adaptive Control of Thought, Rational) theory, which explained how the SA was produced. To verify the validity of this model, 28 subjects performed an instrument supervision task under different experiment conditions. Situation Awareness Global Assessment Technique (SAGAT), 10-dimensional Situational Awareness Rating Technique (10-D SART), performance measure and eye movement measure were adopted for evaluating SAs under different conditions. Statistical analysis demonstrated that the changing trend of SA calculated by this model was highly correlated with the experimental results. Therefore the situational awareness model can provide a reference for designing new cockpit display interfaces and help reducing human errors.