Shuang Liu

Background: Stress refers to the non - specific response that occurs in an organism when it is subjected to stressful stimuli. When fighter pilots execute high-stress missions, they are likely to experience stress responses. These responses may have a negative impact on the cognitive decision-making processes of the pilots, subsequently undermining the mission effectiveness. However, to date, there is a dearth of methodologies for evaluating the in-flight stress states of pilots. The aim of this research is to establish an assessment model by using the real physiological data of pilots in flight to predict their stress state during flight. Method: This study utilized the in - flight physiological data of 32 pilots aged between 22 and 30 years old. The data included electrocardiogram (ECG) data, respiratory data, and human body acceleration data. By analyzing the differences in the physiological characteristics of pilots under various stress conditions, sensitive physiological indicators corresponding to different stress states were screened out. Using electrocardiogram (ECG) and respiratory indices that are sensitive to stress changes as model inputs, a stress state assessment model was constructed via machine learning techniques. The performance of the model was evaluated using accuracy, sensitivity, specificity, and the F1 - score. Result: Screen out ten electrocardiogram (ECG) indicators that are sensitive to the stress level of pilots, which are Mean HR, MeanRR, SDNN, RMSSD, pNN50, pNN20, LF/HF, LFn, HFn and SD1/SD2. Screen out five respiratory indicators that are sensitive to the stress level of pilots, which are Mean Rsp, EB1, EB2, EB3 and EB4. The binary classification assessment model of stress state, which was constructed using the ensemble learning approach, achieved an accuracy of 95.56% in the five - fold cross - validation. Conclusion: This study effectively accomplished the assessment of pilots’ stress states based on physiological characteristics. It is conducive to optimizing the stress training strategies for pilots and enhancing their capabilities to cope with and manage stress. This research has certain theoretical significance and engineering application value for improving the performance of flight missions and ensuring flight safety.

An experiment with a two interaction modalities (traditional: touch; novel: touch–speech) × two task complexities (low: visual single task; high: audio–visual dual task) within-subjects design was conducted to observe alterations in crew performance (including task performance, subjective workload, and eye responses) in a typical planning task-based on a high-fidelity special vehicle simulation platform. The results revealed that (1) compared to the traditional interaction modality, the novel interaction modality significantly improved task performance, reduced subjective workload, increased mean peak saccade velocity, and decreased fixation entropy; (2) under high task complexity, subjective workload, mean pupil diameter, and the nearest neighbor index showed significant increases, while no significant decline in task performance was observed; (3) no interaction effect of crew performance was observed between interaction modality and task complexity. The foregoing results imply that (1) the novel interaction modality incorporating speech input exhibits advantages over the traditional touch-based modality in terms of enhancing task performance (over 45% improvement) and reducing cognitive workload; (2) leveraging dual-channel audio–visual information processing facilitates the maintenance of task performance under high task complexity and multi-tasking demands; (3) eye movement characteristics may serve as informative indicators for evaluating the benefits of speech-based interaction and the effectiveness of cognitive resource allocation under high-complexity task conditions. The results can provide a basis for the design of the display and control interface in special vehicles.

Reliable assessment of team performance based on teamwork characteristics is crucial for optimizing maritime teamwork and ensuring maritime safety. However, the relationship between teamwork characteristics and team performance remains unclear. This study explored teamwork characteristics that contribute to good team performance, and constructed a team performance assessment model based on teamwork characteristics. A total of 84 qualified participants were recruited and divided into 21 teams to perform underwater collaborative tasks on a maritime simulator. Eye movement, communication, scale, and team performance data were collected and divided into input features and outcome features. Based on input features, important teamwork characteristics were obtained, showing that teams with good team performance presented adequate situation awareness for judgment and decision, excellent task management, appropriate decision making, and efficient communication. In addition, machine learning algorithms were applied to train classification models, and the support vector machine (SVM) exhibited the best results, with an accuracy of 83.3%. The team performance assessment model offers an effective auxiliary tool for maritime training to evaluate training effects as well as create more efficient training programs, and is beneficial for optimizing teamwork skills, improving team performance, and ensuring maritime safety.

Effective integration of human factors and systems engineering has become a technical challenge that constrains the full realization of human performance in unmanned aircraft systems (UAS) for urban air transportation. To address this challenge, breakthroughs are needed in key technologies related to human-system integration (HSI) of UAS. Based on literature review and industry practices, unique HF challenges of UAS are identified, and two research issues, HSI analysis throughout UAS development lifecycle and HSI practice under UAS typical lifecycle stages, are summarized. To address these issues, a model-based human-system integration (MBHSI) design framework is proposed for the UAS development lifecycle, along with an HSI practice framework for UAS under typical human readiness levels. The HSI design and practice framework can provide references for HF design of UAS in urban air transportation.

Situation awareness (SA) discrimination is significant, allowing for the pilot to maintain task performance and ensure flight safety, especially during high-stress flight tasks. Although previous research has attempted to identify and classify SA, existing SA discrimination models are predominantly binary and rely on traditional machine learning methods with limited physiological modalities. The current study aimed to construct a triple-class SA discrimination model for pilots facing high-stress tasks. To achieve this, a flight simulation experiment under typical high-stress tasks was carried out and deep learning algorithms (multilayer perceptron (MLP) and the attention mechanism) were utilized. Specifically, eye-tracking (ET), heart rate variability (HRV), and electroencephalograph (EEG) modalities were chosen as the model’s input features. Comparing the unimodal models, the results indicate that EEG modality surpasses ET and HRV modalities, and the attention mechanism structure has advantageous implications for processing the EEG modalities. The most superior model fused the three modalities at the decision level, with two MLP backbones and an attention mechanism backbone, achieving an accuracy of 83.41% and proving that the model performance would benefit from multimodal fusion. Thus, the current research established a triple-class SA discrimination model for pilots, laying the foundation for the real-time evaluation of SA under high-stress aerial operating conditions and providing a reference for intelligent cockpit design and dynamic human–machine function allocation.

The ship information system has the characteristics of complex operation and large amount of information. Due to the limited cognitive ability of the operator, the overload of cognitive resources will reduce the efficiency of the task completion of the complex information system. Existing studies have provided a set of indicators to characterize the change of operational status of maritime personnel, but there is a lack of specific research on the sensitivity and operability of each indicator. In this context, based on the indicators representing the change of operational status of maritime personnel, this study adopts the form of questionnaire to carry out the sensitivity experiment of indicators. In the experiment, the first and second indexes were scored respectively, and the experimental results showed that performance and subjective scores were obviously better than physiological data measurement methods. In terms of physiological data, the scores of eye movement, electrocardiogram and facial expression were better than those of electrodermal activity and electroencephalogram. The conclusion of this study can be used to select the operational status change index of ship information system operators.