The Experts below are selected from a list of 9600 Experts worldwide ranked by ideXlab platform

Hernan Lira - One of the best experts on this subject based on the ideXlab platform.

  • using psychophysiological sensors to assess Mental Workload during web browsing
    Sensors, 2018
    Co-Authors: Angel Jimenezmolina, Cristian Retamal, Hernan Lira
    Abstract:

    Knowledge of the Mental Workload induced by a Web page is essential for improving users’ browsing experience. However, continuously assessing the Mental Workload during a browsing task is challenging. To address this issue, this paper leverages the correlation between stimuli and physiological responses, which are measured with high-frequency, non-invasive psychophysiological sensors during very short span windows. An experiment was conducted to identify levels of Mental Workload through the analysis of pupil dilation measured by an eye-tracking sensor. In addition, a method was developed to classify Mental Workload by appropriately combining different signals (electrodermal activity (EDA), electrocardiogram, photoplethysmo-graphy (PPG), electroencephalogram (EEG), temperature and pupil dilation) obtained with non-invasive psychophysiological sensors. The results show that the Web browsing task involves four levels of Mental Workload. Also, by combining all the sensors, the efficiency of the classification reaches 93.7%.

  • Using Psychophysiological Sensors to Assess Mental Workload in Web Browsing
    2017
    Co-Authors: Angel Jimenez-molina, Cristian Retamal, Hernan Lira
    Abstract:

    The Mental Workload induced by a Web page is essential for improving the user’s browsing experience. However, continuously assessing the Mental Workload during a browsing task is challenging. In order to face this issue, this paper leverages the correlation between stimuli and physiological responses, which are measured with high-frequency, non-invasive psychophysiological sensors during very short span windows. An experiment was conducted to identify levels of Mental Workload through the analysis of pupil dilation measured by an eye-tracking sensor. In addition, a method was developed to classify real-time Mental Workload by appropriately combining different signals (electrodermal activity (EDA), electrocardiogram, photoplethysmography (PPG), electroencephalogram (EEG), temperature and eye gaze) obtained with non-invasive psychophysiological sensors. The results show that the Web browsing task involves on average four levels of Mental Workload. Also, by combining EEG with the PPG and EDA, the accuracy of the classification reaches 95.73 %.

Angel Jimenezmolina - One of the best experts on this subject based on the ideXlab platform.

  • using psychophysiological sensors to assess Mental Workload during web browsing
    Sensors, 2018
    Co-Authors: Angel Jimenezmolina, Cristian Retamal, Hernan Lira
    Abstract:

    Knowledge of the Mental Workload induced by a Web page is essential for improving users’ browsing experience. However, continuously assessing the Mental Workload during a browsing task is challenging. To address this issue, this paper leverages the correlation between stimuli and physiological responses, which are measured with high-frequency, non-invasive psychophysiological sensors during very short span windows. An experiment was conducted to identify levels of Mental Workload through the analysis of pupil dilation measured by an eye-tracking sensor. In addition, a method was developed to classify Mental Workload by appropriately combining different signals (electrodermal activity (EDA), electrocardiogram, photoplethysmo-graphy (PPG), electroencephalogram (EEG), temperature and pupil dilation) obtained with non-invasive psychophysiological sensors. The results show that the Web browsing task involves four levels of Mental Workload. Also, by combining all the sensors, the efficiency of the classification reaches 93.7%.

Xiaoru Wanyan - One of the best experts on this subject based on the ideXlab platform.

  • Mental Workload prediction based on attentional resource allocation and information processing.
    Bio-medical materials and engineering, 2020
    Co-Authors: Xu Xiao, Xiaoru Wanyan, Damin Zhuang
    Abstract:

    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.

  • Mental Workload Classification Method Based on EEG Independent Component Features
    Applied Sciences, 2020
    Co-Authors: Hongquan Qu, Yiping Shan, Liping Pang, Jie Zhang, Xiaoru Wanyan
    Abstract:

    Excessive Mental Workload will reduce work efficiency, but low Mental Workload will cause a waste of human resources. It is very significant to study the Mental Workload status of operators. The existing Mental Workload classification method is based on electroencephalogram (EEG) features, and its classification accuracy is often low because the channel signals recorded by the EEG electrodes are a group of mixed brain signals, which are similar to multi-source mixed speech signals. It is not wise to directly analyze the mixed signals in order to distinguish the feature of EEG signals. In this study, we propose a Mental Workload classification method based on EEG independent components (ICs) features, which borrows from the blind source separation (BSS) idea of mixed speech signals. This presented method uses independent component analysis (ICA) to obtain pure signals, i.e., ICs. The energy features of ICs are directly extracted for classifying the Mental Workload, since this method directly uses ICs energy features for feature extraction. Compared with the existing solution, the proposed method can obtain better classification results. The presented method might provide a way to realize a fast, accurate, and automatic Mental Workload classification.

  • Pilots' Mental Workload prediction based on timeline analysis.
    Technology and Health Care, 2020
    Co-Authors: Xiaoru Wanyan, Xu Xiao, Jingquan Zhao, Ya Duan
    Abstract:

    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.

  • Pilot Mental Workload Prediction Model Based on Information Display Interface
    2015 Seventh International Conference on Measuring Technology and Mechatronics Automation, 2015
    Co-Authors: Xiaoru Wanyan, Damin Zhuang
    Abstract:

    To predict the changes of Mental Workload for the pilot, a Mental Workload prediction model based on the information display interface, which comprehensively considering the influences of time pressure, information intensity and multidimensional information coding on Mental Workload, was proposed. In order to verify the validity of the model, 20 subjects performed an indicators monitoring task under different task conditions. Performance measure, subjective measure and physiological measure were adopted for evaluation the Mental Workload. The integrated experiMental results reveal that the changing trend of Mental Workload calculated by the theoretical model is relatively highly correlated with the practical experiMental results. This Mental Workload prediction model will provide a reference for the ergonomics evaluation and optimization design of cockpit display interface.

  • Study on Comprehensive Evaluation Method of Pilot Mental Workload
    2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics, 2014
    Co-Authors: Huan Zhang, Xiaoru Wanyan, Damin Zhuang, Xu Wu
    Abstract:

    Diverse flight situations will have different influences on the function of a pilot's brain. To study these influences and confirm the sensitivity of related measurements, flight simulation tasks are carried out on an aircraft cockpit ergonomics evaluation experiMental base. Subjects are required to monitor the changes of flight information and make corresponding flight operations simultaneously. Different quantities of abnormal information on the head-up display are handled so that pilot Mental Workload can be divided into four levels. Subjective assessment, behavior performance and physiological measurements are comprehensively applied to evaluate subjects' Mental Workload. Experiment results indicate that both subjective assessment based on NASA Task Load Index (TLX) and behavior performance can reflect the changes of pilot Mental Workload, heart rate variability, mean pupil diameter and eyelid opening indices are sensitive to Mental Workload in terms of physiological measurements. Combining all above measurements, a comprehensive evaluation method can provide a reasonable basis of pilot Mental Workload assessment and classification under complex flight tasks.

Dick De Waard - One of the best experts on this subject based on the ideXlab platform.

  • monitoring drivers Mental Workload in driving simulators using physiological measures
    Accident Analysis & Prevention, 2010
    Co-Authors: Karel Brookhuis, Dick De Waard
    Abstract:

    Many traffic accidents are caused by, or at least related to, inadequate Mental Workload, when it is either too low (vigilance) or too high (stress). Creating variations in Mental Workload and accident-prone driving for research purposes is difficult in the real world. In driving simulators the measurement of driver Mental Workload is relatively easily conducted by means of physiological measures, although good research skills are required and it is time-consuming. The fact that modern driving simulator environments are laboratory-equivalent nowadays allows full control with respect to environMental conditions, scenarios and stimuli, and enables physiological measurement of parameters of Mental Workload such as heart rate and brain activity. Several examples are presented to illustrate the potential of modern high-standard driving simulator environments regarding the monitoring of drivers' Mental Workload during task performance.

  • Monitoring drivers' Mental Workload in driving simulators using physiological measures
    Accident Analysis and Prevention, 2010
    Co-Authors: Karel A. Brookhuis, Dick De Waard
    Abstract:

    Many traffic accidents are caused by, or at least related to, inadequate Mental Workload, when it is either too low (vigilance) or too high (stress). Creating variations in Mental Workload and accident-prone driving for research purposes is difficult in the real world. In driving simulators the measurement of driver Mental Workload is relatively easily conducted by means of physiological measures, although good research skills are required and it is time-consuming. The fact that modern driving simulator environments are laboratory-equivalent nowadays allows full control with respect to environMental conditions, scenarios and stimuli, and enables physiological measurement of parameters of Mental Workload such as heart rate and brain activity. Several examples are presented to illustrate the potential of modern high-standard driving simulator environments regarding the monitoring of drivers' Mental Workload during task performance. © 2009 Elsevier Ltd. All rights reserved.

  • On the measurement of driver Mental Workload
    1997
    Co-Authors: Dick De Waard, Karel A. Brookhuis
    Abstract:

    Driver Mental Workload and its assessment are becoming more and more important with increased penetration of 'Road Transport Informatics' and with changes in driver population and capability, i.e. the increasing number of elderly drivers. In particular newly developed electronic driver support systems, such as route guidance systems, are being tested now for their potential negative effect on driving performance because of the additional Mental Workload they impose. The increased attention for Mental Workload techniques has made obvious that there are many factors that complicate accurate assessment of Workload in the field. Firstly, there are different causes for increased Workload. Both an impaired driver state, e.g. as a result of the use of alcohol, and increased complexity, e.g. an additional task that has to be performed, lead to elevated driver Mental Workload. Secondly, the measurement techniques themselves are differentially sensitive to changes in Workload. Thirdly, the driving task is to a large extent a self-paced task. This means that the driving speed chosen or the accuracy in lane-keeping are adapted by the driver, not only on the basis of external demands but are also dependent upon strategy and self-set goals. Not only these factors in isolation, but also their interaction complicate the measurement of drivers' Mental Workload. In an effort to understand this, Mental Workload, task demands and performance were related to each other in a simple model, which was presented. Several of the problems that are encountered when trying to measure Mental Workload are put in a different perspective in this model. For the covering abstract, see IRRD 896859.

Fang Chen - One of the best experts on this subject based on the ideXlab platform.

  • ICDAR - Mental Workload Classification via Online Writing Features
    2013 12th International Conference on Document Analysis and Recognition, 2013
    Co-Authors: Kun Yu, Julien Epps, Fang Chen
    Abstract:

    Mental Workload is an important factor during writing, which may affect the writing efficiency and user experience. This paper aims at a method to classify the Mental Workload levels during writing process, via examination of online writing features in a two-stage algorithm structure. At the first stage, a curvature tracking method is applied to the handwriting script, to examine the curvature for individual writing points. Then a selection process allocates writing points into subsets, each corresponding to one curvature span. The second stage extracts velocity features, used to characterize Mental Workload, from points in each curvature span. A Parzen-window classifier is applied on velocity features from each curvature span. The classification decisions from individual classifiers are fused with a selective voting scheme for the overall Mental Workload classification decision. This paper finally discusses the classification accuracy for three Mental Workload levels and compares it with previous work.

  • Mental Workload Classification via Online Writing Features
    2013 12th International Conference on Document Analysis and Recognition, 2013
    Co-Authors: Kun Yu, Julien Epps, Fang Chen
    Abstract:

    Mental Workload is an important factor during writing, which may affect the writing efficiency and user experience. This paper aims at a method to classify the Mental Workload levels during writing process, via examination of online writing features in a two-stage algorithm structure. At the first stage, a curvature tracking method is applied to the handwriting script, to examine the curvature for individual writing points. Then a selection process allocates writing points into subsets, each corresponding to one curvature span. The second stage extracts velocity features, used to characterize Mental Workload, from points in each curvature span. A Parzen-window classifier is applied on velocity features from each curvature span. The classification decisions from individual classifiers are fused with a selective voting scheme for the overall Mental Workload classification decision. This paper finally discusses the classification accuracy for three Mental Workload levels and compares it with previous work.