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.

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 Experimental Analysis of Driver Visual Characteristics in Urban Tunnels

2021 ◽  
Vol 11 (9) ◽  
pp. 4274
Author(s):  
Song Fang ◽  
Jianxiao Ma
Keyword(s):  
Traffic Safety ◽  
Clustering Algorithm ◽  
Pupil Diameter ◽  
Data Fitting ◽  
Eye Tracker ◽  
Tunnel Length ◽  
Point Distribution ◽  
Change Rate ◽  
Visual Characteristics ◽  
Diameter Change

Through an urban tunnel-driving experiment, this paper studies the changing trend of drivers’ visual characteristics in tunnels. A Tobii Pro Glasses 2 wearable eye tracker was used to measure pupil diameter, scanning time, and fixation point distribution of the driver during driving. A two-step clustering algorithm and the data-fitting method were used to analyze the experimental data. The results show that the univariate clustering analysis of the pupil diameter change rate of drivers has poor discrimination because the pupil diameter change rate of drivers in the process of “dark adaptation” is larger, while the pupil diameter change rate of drivers in the process of “bright adaptation” is relatively smooth. The univariate and bivariate clustering results of drivers’ pupil diameters were all placed into three categories, with reasonable distribution and suitable differentiation. The clustering results accurately corresponded to different locations of the tunnel. The clustering method proposed in this paper can identify similar behaviors of drivers at different locations in the transition section at the tunnel entrance, the inner section, and the outer area of the tunnel. Through data-fitting of drivers’ visual characteristic parameters in different tunnels, it was found that a short tunnel, with a length of less than 1 km, has little influence on visual characteristics when the maximum pupil diameter is small, and the percentage of saccades is relatively low. An urban tunnel with a length between 1 and 2 km has a significant influence on visual characteristics. In this range, with the increase in tunnel length, the maximum pupil diameter increases significantly, and the percentage of saccades increases rapidly. When the tunnel length exceeds 2 km, the maximum pupil diameter does not continue to increase. The longer the urban tunnel, the more discrete the distribution of drivers’ gaze points. The research results should provide a scientific basis for the design of urban tunnel traffic safety facilities and traffic organization.

An eye-tracking study on the effect of infographic structures on viewer’s comprehension and cognitive load

2017 ◽  
Vol 17 (3) ◽  
pp. 257-266 ◽  
Author(s):  
Azam Majooni ◽  
Mona Masood ◽  
Amir Akhavan
Keyword(s):  
Eye Tracking ◽  
Cognitive Load ◽  
Pupil Diameter ◽  
Information Graphics ◽  
Basic Premise ◽  
Quantitative Evidence ◽  
The Gaze ◽  
Movement Data ◽  
Graphical Design ◽  
Tracking Devices

The basic premise of this research is investigating the effect of layout on the comprehension and cognitive load of the viewers in the information graphics. The term ‘Layout’ refers to the arrangement and organization of the visual and textual elements in a graphical design. The experiment conducted in this study is designed based on two stories and each one of these stories is presented with two different layouts. During the experiment, eye-tracking devices are applied to collect the gaze data including the eye movement data and pupil diameter fluctuation. In the research on the modification of the layouts, contents of each story are narrated using identical visual and textual elements. The analysis of eye-tracking data provides quantitative evidence concerning the change of layout in each story and its effect on the comprehension of participants and variation of their cognitive load. In conclusion, it can be claimed that the comprehension from the zigzag form of the layout was higher with a less imposed cognitive load.

scholarly journals Auditory Working Memory Load Impairs Visual Ventral Stream Processing: Toward a Unified Model of Attentional Load

2010 ◽  
Vol 22 (3) ◽  
pp. 437-446 ◽  
Author(s):  
Jane Klemen ◽  
Christian Büchel ◽  
Mira Bühler ◽  
Mareike M. Menz ◽  
Michael Rose
Keyword(s):  
Working Memory ◽  
Cognitive Control ◽  
Cognitive Load ◽  
Human Performance ◽  
Memory Load ◽  
Matching Task ◽  
Load Theory ◽  
Attentional Load ◽  
Relevant Task ◽  
Task Irrelevant

Attentional interference between tasks performed in parallel is known to have strong and often undesired effects. As yet, however, the mechanisms by which interference operates remain elusive. A better knowledge of these processes may facilitate our understanding of the effects of attention on human performance and the debilitating consequences that disruptions to attention can have. According to the load theory of cognitive control, processing of task-irrelevant stimuli is increased by attending in parallel to a relevant task with high cognitive demands. This is due to the relevant task engaging cognitive control resources that are, hence, unavailable to inhibit the processing of task-irrelevant stimuli. However, it has also been demonstrated that a variety of types of load (perceptual and emotional) can result in a reduction of the processing of task-irrelevant stimuli, suggesting a uniform effect of increased load irrespective of the type of load. In the present study, we concurrently presented a relevant auditory matching task [n-back working memory (WM)] of low or high cognitive load (1-back or 2-back WM) and task-irrelevant images at one of three object visibility levels (0%, 50%, or 100%). fMRI activation during the processing of the task-irrelevant visual stimuli was measured in the lateral occipital cortex and found to be reduced under high, compared to low, WM load. In combination with previous findings, this result is suggestive of a more generalized load theory, whereby cognitive load, as well as other types of load (e.g., perceptual), can result in a reduction of the processing of task-irrelevant stimuli, in line with a uniform effect of increased load irrespective of the type of load.

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

HUMAN FACTOR ANALYSIS OF CONTAINER VESSEL’S GROUNDING ACCIDENTS

2021 ◽  
Vol 159 (A1) ◽  
Author(s):  
U Yildirim ◽  
O Ugurlu ◽  
E Basar ◽  
E Yuksekyildiz
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Human Error ◽  
Human Performance ◽  
Human Factor ◽  
Study Data ◽  
Team Management ◽  
Contribution Rate ◽  
Physical Problems ◽  
Mental Problems

Investigation on maritime accidents is a very important tool in identifying human factor-related problems. This study examines the causes of accidents, in particular the reasons for the grounding of container ships. These are analysed and evaluation according to the contribution rate using the Monte Carlo simulation. The OpenFTA program is used to run the simulation. The study data are obtained from 46 accident reports from 1993 to 2011. The data were prepared by the International Maritime Organization (IMO) Global Integrated Shipping Information System (GISIS). The GISIS is one of the organizations that investigate reported accidents in an international framework and in national shipping companies. The Monte Carlo simulation determined a total of 23.96% human error mental problems, 26.04% physical problems, 38.58% voyage management errors, and 11.42% team management error causes. Consequently, 50% of the human error is attributable to human performance disorders, while 50% team failure has been found.

scholarly journals Comparing continual task learning in minds and machines

2018 ◽  
Vol 115 (44) ◽  
pp. E10313-E10322 ◽  
Author(s):  
Timo Flesch ◽  
Jan Balaguer ◽  
Ronald Dekker ◽  
Hamed Nili ◽  
Christopher Summerfield
Keyword(s):  
Human Error ◽  
Human Performance ◽  
Deep Neural Networks ◽  
Artificial Agents ◽  
Trial And Error ◽  
Stimulus Space ◽  
Cognitive Mechanisms ◽  
Error Training ◽  
Multiple Tasks ◽  
Continual Learning

Humans can learn to perform multiple tasks in succession over the lifespan (“continual” learning), whereas current machine learning systems fail. Here, we investigated the cognitive mechanisms that permit successful continual learning in humans and harnessed our behavioral findings for neural network design. Humans categorized naturalistic images of trees according to one of two orthogonal task rules that were learned by trial and error. Training regimes that focused on individual rules for prolonged periods (blocked training) improved human performance on a later test involving randomly interleaved rules, compared with control regimes that trained in an interleaved fashion. Analysis of human error patterns suggested that blocked training encouraged humans to form “factorized” representation that optimally segregated the tasks, especially for those individuals with a strong prior bias to represent the stimulus space in a well-structured way. By contrast, standard supervised deep neural networks trained on the same tasks suffered catastrophic forgetting under blocked training, due to representational interference in the deeper layers. However, augmenting deep networks with an unsupervised generative model that allowed it to first learn a good embedding of the stimulus space (similar to that observed in humans) reduced catastrophic forgetting under blocked training. Building artificial agents that first learn a model of the world may be one promising route to solving continual task performance in artificial intelligence research.

scholarly journals USING MOBILE LASER SCANNING DATA FOR FEATURES EXTRACTION OF HIGH ACCURACY DRIVING MAPS

2016 ◽  
Vol XLI-B3 ◽  
pp. 433-439 ◽  
Author(s):  
Bisheng Yang ◽  
Yuan Liu ◽  
Fuxun Liang ◽  
Zhen Dong
Keyword(s):  
Laser Scanning ◽  
Human Error ◽  
Traffic Accidents ◽  
Three Dimensional ◽  
Point Clouds ◽  
High Accuracy ◽  
3D Point Clouds ◽  
Assistant Systems ◽  
Novel Method ◽  
High Flexibility

High Accuracy Driving Maps (HADMs) are the core component of Intelligent Drive Assistant Systems (IDAS), which can effectively reduce the traffic accidents due to human error and provide more comfortable driving experiences. Vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. This paper proposes a novel method to extract road features (e.g., road surfaces, road boundaries, road markings, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, vehicles and so on) for HADMs in highway environment. Quantitative evaluations show that the proposed algorithm attains an average precision and recall in terms of 90.6% and 91.2% in extracting road features. Results demonstrate the efficiencies and feasibilities of the proposed method for extraction of road features for HADMs.

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.

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