scholarly journals Cognitive Load Identification of Pilots Based on Physiological-Psychological Characteristics in Complex Environments

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Haibo Wang ◽  
Naiqi Jiang ◽  
Ting Pan ◽  
Haiqing Si ◽  
Yao Li ◽  
...  
Keyword(s):  
Cognitive Load ◽  
Flight Control ◽  
Human Error ◽  
Pearson Correlation ◽  
Physiological Data ◽  
Support Vector ◽  
Flight Safety ◽  
Environment Interaction ◽  
Load Identification ◽  
Traffic Pattern

Cognitive load is generated by pilots in the process of information cognition about aircraft control, and it is closely related to flight safety. Cognitive load is the physiological and psychological need that a pilot produces when completing a mission. Therefore, it is meaningful to study the dynamic identification of the cognitive load of the pilot under the complex human-aircraft-environment interaction. In this paper, the airfield traffic pattern flight simulation experiment was designed and used to obtain the ECG physiological and NASA-TLX psychological data. The wavelet transform preprocessing and mathematical statistics analysis were applied on them, respectively. Furthermore, the Pearson correlation analysis method is used to select the characteristic indicators of psycho-physiological data after preprocessing. Based on the psycho-physiological characteristic indicators, the pilot’s cognitive load identification model is constructed by combining RNN and LSTM. The results of this study are more accurate compared with the cognitive load identification models established by other methods such as RNN neural network and support vector machine. This research is able to provide a useful reference for preventing and reduction of human error caused by the cognitive load during flight missions. It will be potential to realize intelligent control of aircraft cockpit, improving the flight control behavior and maintaining flight safety.

scholarly journals A framework to estimate cognitive load using physiological data

2020 ◽  
Author(s):  
Muneeb Imtiaz Ahmad ◽  
Ingo Keller ◽  
David A. Robb ◽  
Katrin S. Lohan
Keyword(s):  
Cognitive Load ◽  
Human Error ◽  
Human Performance ◽  
Predictive Accuracy ◽  
Pupil Diameter ◽  
High Accuracy ◽  
Physiological Data ◽  
Workload Management ◽  
Different Levels ◽  
Diameter Change

Abstract Cognitive load has been widely studied to help understand human performance. It is desirable to monitor user cognitive load in applications such as automation, robotics, and aerospace to achieve operational safety and to improve user experience. This can allow efficient workload management and can help to avoid or to reduce human error. However, tracking cognitive load in real time with high accuracy remains a challenge. Hence, we propose a framework to detect cognitive load by non-intrusively measuring physiological data from the eyes and heart. We exemplify and evaluate the framework where participants engage in a task that induces different levels of cognitive load. The framework uses a set of classifiers to accurately predict low, medium and high levels of cognitive load. The classifiers achieve high predictive accuracy. In particular, Random Forest and Naive Bayes performed best with accuracies of 91.66% and 85.83% respectively. Furthermore, we found that, while mean pupil diameter change for both right and left eye were the most prominent features, blinking rate also made a moderately important contribution to this highly accurate prediction of low, medium and high cognitive load. The existing results on accuracy considerably outperform prior approaches and demonstrate the applicability of our framework to detect cognitive load.

Adaptive support vector regression for UAV flight control

2011 ◽  
Vol 24 (1) ◽  
pp. 109-120 ◽  
Author(s):  
Jongho Shin ◽  
H. Jin Kim ◽  
Youdan Kim
Keyword(s):  
Support Vector Regression ◽  
Flight Control ◽  
Support Vector ◽  
Adaptive Support

Driver Cognitive Load Classification Based on Physiological Data—Case Study 7

2021 ◽  
pp. 409-429
Author(s):  
Dengbo He ◽  
Martina Risteska ◽  
Birsen Donmez ◽  
Kaiyang Chen
Keyword(s):  
Cognitive Load ◽  
Physiological Data

CIVIL AVIATION ACCIDENTS AND INCIDENTS CLASSIFIED ACCORDING TO GROUPS OF AVIATION SPECIALISTS

Aviation ◽  
2013 ◽  
Vol 17 (2) ◽  
pp. 76-79 ◽  
Author(s):  
Peter Trifonov-Bogdanov ◽  
Leonid Vinogradov ◽  
Vladimir Shestakov
Keyword(s):  
Human Error ◽  
Human Factor ◽  
Negative Influence ◽  
Civil Aviation ◽  
Flight Safety ◽  
Complex Action ◽  
Primary Parameter ◽  
Aviation Accidents ◽  
Operational Process ◽  
Operational Activities

During an operational process, activity is implemented through an ordered sequence of certain actions united by a common motive. Actions can be simple or complex. Simple actions cannot be split into elements having independent objectives. Complex actions can be presented in the form of a set of simple actions. If the logical organisation of this set is open, a complex action can be described as an algorithm consisting of simple actions. That means various kinds of operational activities develop from the same simple and typical actions, but in various sequences. Therefore, human error is always generated by a more elementary error of action. Thus, errors of action are the primary parameter that is universal for any kind of activity of an aviation specialist and can serve as a measure for estimating the negative influence of the human factor (HF) on flight safety. Aviation personnel are various groups of experts having various specialisations and working in various areas of civil aviation. It is obvious that their influence on conditions is also unequal and is defined by their degree of interaction with the performance of flights. In this article, the results of an analysis of air incidents will be presented.

scholarly journals Classification of Hot Spots using XGBoost and LightGBM Algorithms

2020 ◽  
Vol 9 (5) ◽  
pp. 722-724
Keyword(s):  
Computational Methods ◽  
Protein Interactions ◽  
Hot Spots ◽  
Cell Metabolism ◽  
Pearson Correlation ◽  
Classification Performance ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting ◽  
Hub Proteins

Protein-Protein Interactions referred as PPIs perform significant role in biological functions like cell metabolism, immune response, signal transduction etc. Hot spots are small fractions of residues in interfaces and provide substantial binding energy in PPIs. Therefore, identification of hot spots is important to discover and analyze molecular medicines and diseases. The current strategy, alanine scanning isn't pertinent to enormous scope applications since the technique is very costly and tedious. The existing computational methods are poor in classification performance as well as accuracy in prediction. They are concerned with the topological structure and gene expression of hub proteins. The proposed system focuses on hot spots of hub proteins by eliminating redundant as well as highly correlated features using Pearson Correlation Coefficient and Support Vector Machine based feature elimination. Extreme Gradient boosting and LightGBM algorithms are used to ensemble a set of weak classifiers to form a strong classifier. The proposed system shows better accuracy than the existing computational methods. The model can also be used to predict accurate molecular inhibitors for specific PPIs

scholarly journals Quantitative Influence and Performance Analysis of VR Laparoscopic Surgical Training System

2021 ◽  
Author(s):  
Peng Yu ◽  
Junjun Pan ◽  
Zhaoxue Wang ◽  
Yang Shen ◽  
Jialun Li ◽  
...  
Keyword(s):  
Medical Students ◽  
Performance Analysis ◽  
Cognitive Load ◽  
Surgical Training ◽  
Clinical Education ◽  
Training System ◽  
Physiological Data ◽  
Flow Experience ◽  
And Performance ◽  
Quantitative Influence

Abstract Background VR surgery training becomes a trend in clinical education. Many research papers validate the effectiveness of VR based surgical simulators in training surgeons. However, most existing papers employ subjective methods to study the residents’ surgical skills improvement. Few of them investigates how to substantially improve the surgery skills on specific dimensions.Methods In this paper, we resort to physiological approaches to objectively research quantitative influence and performance analysis of VR laparoscopic surgical training system for medical students. 41 participants were recruited from a pool of medical students. They conducted four pre and post experiments in the training box. In the middle of pre and post experiments, they were trained on VR laparoscopic surgery simulators (VRLS). When conducting pre and post experiments, their operation process and physiological data (heart rate and electroencephalogram) are recorded. Their performance is graded by senior surgeons using newly designed hybrid standards for fundamental tasks and GOALS standards for colon resection tasks. Finally, the participants were required to fill the questionnaires about their cognitive load and flow experience.Results The results show that the VRLS could highly improve medical students' performance (p < 0.01) especially in depth perception and enable the participants to obtain flow experience with a lower cognitive load.Conclusion The performance of participants is negatively correlated with cognitive load through quantitatively physiological analysis. This might provide a new way of assessing skill acquirement.

Algorithmic support of low-altitude aircraft flight based on the hazard analysis of the flight situation

2021 ◽  
Author(s):  
V.A. Malyshev ◽  
A.S. Leontyev ◽  
S.P. Poluektov ◽  
Е.М. Volotov
Keyword(s):  
Adaptive Control ◽  
Flight Control ◽  
Control Algorithm ◽  
Hazard Analysis ◽  
Critical Value ◽  
Control Mode ◽  
Manual Control ◽  
Flight Safety ◽  
Low Altitude ◽  
Adaptive Control Algorithm

Low-altitude flight of an aircraft is an effective, but at the same time, a very complex tactical technique, during which the crew does not always have the opportunity to timely recognize the occurrence of an abnormal case, determine the way out of it and counteract an aviation accident development. Despite many advantages of the automatic mode of low-altitude flight performing, its practical implementation is associated with a number of features and disadvantages, which determined the preference for the manual mode of low-altitude flight control. These are the presence of telltale factors, limited ability of performing flights at night and in difficult weather conditions, insufficient reliability etc. The considered features determined the relevance of the of low-altitude flight safety ensuring problem in relation to the manual control mode. As a result of an experimental study of the low-altitude flight performing process in a manual control mode, it was found that when performing manually-controlled low-altitude flight, a hazard assessment of the flight situation becomes pivotal. However the crew being under such conditions is not always able to correctly assess the flight situation hazard due to a combination of objective reasons. The current state of the adaptive and on-board flight safety systems theory makes it possible to increase the safety of the manuallycontrolled low-altitude flight by using adaptive control algorithms based on the flight situation hazard assessment. To solve this problem an adaptive control algorithm is proposed that ensures the formation of a security corridor in the longitudinal control channel, where the upper limit is determined by the critical value of the aircraft detection hazard, and the lower limit is determined by the critical value of the error in maintaining a given flight altitude. For a continuous assessment of the flight situation hazard and the timely formation of control signals the complex information about the current true flight altitude and the foreground is needed. Taking into account the peculiarities of low-altitude flight a digital terrain map containing data on natural and artificial obstacles along the flight route is a more rational source of information, that will make it possible to predict the development of the flight situation hazard. The above reasoning makes it possible to form an aircraft low-altitude flight adaptive control algorithm. A distinctive feature of the proposed algorithm is the implementation of a combined control variety where the pilot is provided with ample manual control opportunities within the security corridor, and the automatic flight control system is assigned the role of a safety subsystem that ensures control and timely return of the flight situation to normal flight conditions. The presented algorithm will allow to increase the crew logical-analytical activity information support during continuous analysis of the existing flight situation due to the formation of protective control actions based on the current flight situation hazard analysis.

scholarly journals Investigating Serum and Tissue Expression Identified a Cytokine/Chemokine Signature as a Highly Effective Melanoma Marker

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3680
Author(s):  
Marco Cesati ◽  
Francesca Scatozza ◽  
Daniela D’Arcangelo ◽  
Gian Carlo Antonini-Cappellini ◽  
Stefania Rossi ◽  
...  
Keyword(s):  
Single Molecule ◽  
Learning Algorithm ◽  
Profile Analysis ◽  
Pearson Correlation ◽  
Tissue Expression ◽  
Support Vector ◽  
Expression Data ◽  
Serum Samples ◽  
Tissue Samples ◽  
Mortality And Morbidity

The identification of reliable and quantitative melanoma biomarkers may help an early diagnosis and may directly affect melanoma mortality and morbidity. The aim of the present study was to identify effective biomarkers by investigating the expression of 27 cytokines/chemokines in melanoma compared to healthy controls, both in serum and in tissue samples. Serum samples were from 232 patients recruited at the IDI-IRCCS hospital. Expression was quantified by xMAP technology, on 27 cytokines/chemokines, compared to the control sera. RNA expression data of the same 27 molecules were obtained from 511 melanoma- and healthy-tissue samples, from the GENT2 database. Statistical analysis involved a 3-step approach: analysis of the single-molecules by Mann–Whitney analysis; analysis of paired-molecules by Pearson correlation; and profile analysis by the machine learning algorithm Support Vector Machine (SVM). Single-molecule analysis of serum expression identified IL-1b, IL-6, IP-10, PDGF-BB, and RANTES differently expressed in melanoma (p < 0.05). Expression of IL-8, GM-CSF, MCP-1, and TNF-α was found to be significantly correlated with Breslow thickness. Eotaxin and MCP-1 were found differentially expressed in male vs. female patients. Tissue expression analysis identified very effective marker/predictor genes, namely, IL-1Ra, IL-7, MIP-1a, and MIP-1b, with individual AUC values of 0.88, 0.86, 0.93, 0.87, respectively. SVM analysis of the tissue expression data identified the combination of these four molecules as the most effective signature to discriminate melanoma patients (AUC = 0.98). Validation, using the GEPIA2 database on an additional 1019 independent samples, fully confirmed these observations. The present study demonstrates, for the first time, that the IL-1Ra, IL-7, MIP-1a, and MIP-1b gene signature discriminates melanoma from control tissues with extremely high efficacy. We therefore propose this 4-molecule combination as an effective melanoma marker.

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