The Experts below are selected from a list of 167397 Experts worldwide ranked by ideXlab platform
Michael C. Dorneich - One of the best experts on this subject based on the ideXlab platform.
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Cognitive State Estimation in Mobile Environments
2008Co-Authors: Michael C. Dorneich, Santosh Mathan, Patricia May Ververs, Stephen WhitlowAbstract:ork in the field of augmented cognition began by classifying aspects of Cognitive processing (attention, working memory, executive function, and sensory processing) with well-defined, well-understood laboratory tasks (often referred to informally as “Psych 101” tasks). As researchers have moved from the laboratory environment to the field environment, they have introduced the artifacts (motion, electrical, networking traffic, and disconnect) and stressors (information overload, physical load, competition, and threat of pain) inherent in some operational environments to which augmented cognition systems would be transitioned. The move from the laboratory to mobile field environments brings a number of unique challenges that must be addressed if Cognitive State assessment is to be used successfully in task domains that require the operator to be mobile. Tough sacrifices need to be made, with limitations on the sensors to be used, processing power, and knowledge of the task environment. Therefore, unique techniques must be developed to enable this technology to move beyond sedentary operator domains. W
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Supporting Real-Time Cognitive State Classification on a Mobile Individual
Journal of Cognitive Engineering and Decision Making, 2007Co-Authors: Michael C. Dorneich, Santosh Mathan, Patricia May Ververs, Deniz Erdogmus, André Gustavo Adami, Stephen Whitlow, Misha PavelAbstract:The effectiveness of neurophysiologically triggered adaptive systems hinges on reliable and effective signal processing and Cognitive State classification. Although this presents a difficult technical challenge in any context, these concerns are particularly pronounced in a system designed for mobile contexts. This paper describes a neurophysiologically derived Cognitive State classification approach designed for ambulatory task contexts. We highlight signal processing and classification components that render the electroencephalogram (EEG) -based Cognitive State estimation system robust to noise. Field assessments show classification performance that exceeds 70% for all participants in a context that many have regarded as intractable for Cognitive State classification using EEG.
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Iterative Experimental Design to Mature Cognitive State Classification Techniques from the Laboratory to Field Settings
2007Co-Authors: Michael C. Dorneich, Stephen Whitlow, Santosh MathanAbstract:This paper summarizes the research conducted on two parallel and complementary thrusts: Cognitive State assessment (CSA) and mitigations development for augmented Cognition Systems. Cognitive workload classification research has been largely been limited to laboratory contexts with well defined tasks. However, as the technology matures, it is important to evaluate real-time, EEG- and ECG-based Cognitive workload classification techniques when they are fully subject to the noise, motion, weather, and wide range of physical and Cognitive tasks inherent in dismounted operational environments. The hardware and software infrastructure must reliably collect clean sensor data, mobile processing is needed to log and process the data, and the experimental design must reliably put participants in the Cognitive States of interest. As mitigations mature, automation etiquette plays an important role in ameliorating the negative effects of strongly adaptive systems. In addition to the practical and system configuration challenges faced when moving from the laboratory to field studies, there are issues of experimental control and the characterization of Cognitive State in less constrained task environments. A key component of an evaluation is to compare the classification results with "ground truth," i.e. the actual workload experienced by the participant. While in the laboratory it is possible to develop simple tasks where workload is manipulated precisely and consistently, in an unconstrained field environment it becomes substantially harder to manipulate workload precisely and to interpret and assess a user's true Cognitive State without compromising operational realism. Advances in experimental design, data collection protocols, signal processing, classification, mitigation strategies, and experimental ground truth methodology successfully enabled classification of Cognitive workload level in an unconstrained, fully-mobile, free-play operation with Soldiers executing missions in a challenging urban terrain environment.
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An evaluation of real-time Cognitive State classification in a harsh operational environment
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2007Co-Authors: Michael C. Dorneich, Santosh Mathan, Patricia May Ververs, Stephen WhitlowAbstract:This paper describes an evaluation conducted with a full platoon of 32 Soldiers at Aberdeen Proving Grounds' MOUT site in Aberdeen, MD. The objective was to assess the Cognitive workload classification techniques driven by neuro-physiological (EEG) and physiological (ECG) sensors. In a first ever evaluation of real-time Cognitive monitoring in the harsh operational environment, the assessment culminated in a three phase, 24-hour mission consisting of a coordinated Route Reconnaissance, a Cordon and Search of a village, and a Hasty Defense operation. Task load levels were manipulated by introducing unexpected and unplanned events requiring re-planning and extensive coordination by the leadership (high task load) as well as lulls in the activity in which part missions were executed flawlessly with little variations on the preplanned, well versed drill (low task load). Four leaders (Platoon Leader, Platoon Sergeant, Squad Leader 1, and Squad Leader 2) were equipped with sensors to measure and output Cognitive State in real-time. The fused EEG and ECG workload classification approach reached 95% accuracy depending on the individual and the amount of data used to train the classifier. This level of success implies that Augmented Cognition workload assessment tools enable the ability to move beyond subjective workload rating scales, such as NASA TLX and Cooper Harper ratings, to more objective measurements of real-time Cognitive State metrics in almost any conceivable operational environment.
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Evaluation of a Tactile Navigation Cueing System and Real-Time Assessment of Cognitive State
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2006Co-Authors: Michael C. Dorneich, Patricia May Ververs, Stephen Whitlow, Santosh MathanAbstract:This paper details an evaluation of a tactile cueing system that was created to enhance the navigation of a complex route. The complexity of the task along with simultaneously challenging Cognitive tasks also enabled the real-time assessment of Cognitive State in various task load conditions. Honeywell has been working with the US Army's Future Force Warrior program to develop adaptive systems that will effectively manage the available Cognitive resources used for information processing by the dismounted Soldier in highly dynamic, information rich environments. The appropriate allocation of Cognitive resources is key to managing multiple tasks, focusing on the most important ones, and maintaining overall situation awareness. Non-visual navigation support would offload a typically visual task, such as viewing a paper map or computer-based map display, to a sensory channel that is underutilized, tactile sensation. Both benefits and costs to this type of automation support are explored in detail, where the evaluation supports the premise that strong automated support should only be used in high workload situations where the benefits outweigh the costs.
Makoto Takahashi - One of the best experts on this subject based on the ideXlab platform.
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Development of a real-time Cognitive State estimator
Control Engineering Practice, 1995Co-Authors: Makoto Takahashi, M. Kitamura, Hidekazu YoshikawaAbstract:Abstract A real-time Cognitive State estimator based on physiological data has been developed to provide information for a man-machine interface study. The system that had been developed for off-line estimation is first described, to outline the basic idea of Cognitive State estimation based on physiological data. The modification to develop a real-time system and to improve the estimation accuracy is then described as the phase-two study. The results of the numerical experiments showed that the developed system is capable of estimating a human's Cognitive State in real-time with sufficient accuracy.
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Development of Realtime Cognitive State Estimator
IFAC Proceedings Volumes, 1993Co-Authors: Makoto Takahashi, M. Kitamura, Hidekazu YoshikawaAbstract:Abstract The realtime Cognitive State estimator based on the physiological data is now under development to provide information for the Man-Machine Interface study. The system for off-line estimation that has already been developed is first described to outline our basic idea of Cognitive State estimation based on the physiological data. The modification to develop realtime system is then described in detail with the emphasis on the realtime data processing and data transfer methodology The current State of the system development is presented with the preliminary results of the realtime analysis.
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IROS - Neural network for human Cognitive State estimation
Proceedings of IEEE RSJ International Conference on Intelligent Robots and Systems (IROS'94), 1Co-Authors: Makoto Takahashi, M. Kitamura, O. Kubo, H. YoshikawaAbstract:A neural network (NN) has been applied to the human Cognitive State estimation based on the set of physiological measures, heart rate, blood pressure, respiration rate, skin potential response (SPR), blink rate and saccadic eye motion rate have been chosen as the representative metrical indices reflecting human mental State. The qualitative tendencies of these measures have been taken as the inputs of the NN. The human Cognitive States are categorized into six pre-specified States: (1) information acquisition (IA); (2) memory related (MR); (3) thought (TH); (4) motor action (MA); (5) emotion (EM); and (6) others (OT). The adopted network a is three layer feedforward network trained with a backpropagation algorithm with forgetting. Sets of training data for learning have been collected through laboratory experiments, in which the subjects were induced to undergo a specific sequence of Cognitive States. The resultant NN showed superior capability of discriminating human Cognitive States based on the pattern of the physiological measures. >
Stephen Whitlow - One of the best experts on this subject based on the ideXlab platform.
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Cognitive State Estimation in Mobile Environments
2008Co-Authors: Michael C. Dorneich, Santosh Mathan, Patricia May Ververs, Stephen WhitlowAbstract:ork in the field of augmented cognition began by classifying aspects of Cognitive processing (attention, working memory, executive function, and sensory processing) with well-defined, well-understood laboratory tasks (often referred to informally as “Psych 101” tasks). As researchers have moved from the laboratory environment to the field environment, they have introduced the artifacts (motion, electrical, networking traffic, and disconnect) and stressors (information overload, physical load, competition, and threat of pain) inherent in some operational environments to which augmented cognition systems would be transitioned. The move from the laboratory to mobile field environments brings a number of unique challenges that must be addressed if Cognitive State assessment is to be used successfully in task domains that require the operator to be mobile. Tough sacrifices need to be made, with limitations on the sensors to be used, processing power, and knowledge of the task environment. Therefore, unique techniques must be developed to enable this technology to move beyond sedentary operator domains. W
-
Supporting Real-Time Cognitive State Classification on a Mobile Individual
Journal of Cognitive Engineering and Decision Making, 2007Co-Authors: Michael C. Dorneich, Santosh Mathan, Patricia May Ververs, Deniz Erdogmus, André Gustavo Adami, Stephen Whitlow, Misha PavelAbstract:The effectiveness of neurophysiologically triggered adaptive systems hinges on reliable and effective signal processing and Cognitive State classification. Although this presents a difficult technical challenge in any context, these concerns are particularly pronounced in a system designed for mobile contexts. This paper describes a neurophysiologically derived Cognitive State classification approach designed for ambulatory task contexts. We highlight signal processing and classification components that render the electroencephalogram (EEG) -based Cognitive State estimation system robust to noise. Field assessments show classification performance that exceeds 70% for all participants in a context that many have regarded as intractable for Cognitive State classification using EEG.
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Iterative Experimental Design to Mature Cognitive State Classification Techniques from the Laboratory to Field Settings
2007Co-Authors: Michael C. Dorneich, Stephen Whitlow, Santosh MathanAbstract:This paper summarizes the research conducted on two parallel and complementary thrusts: Cognitive State assessment (CSA) and mitigations development for augmented Cognition Systems. Cognitive workload classification research has been largely been limited to laboratory contexts with well defined tasks. However, as the technology matures, it is important to evaluate real-time, EEG- and ECG-based Cognitive workload classification techniques when they are fully subject to the noise, motion, weather, and wide range of physical and Cognitive tasks inherent in dismounted operational environments. The hardware and software infrastructure must reliably collect clean sensor data, mobile processing is needed to log and process the data, and the experimental design must reliably put participants in the Cognitive States of interest. As mitigations mature, automation etiquette plays an important role in ameliorating the negative effects of strongly adaptive systems. In addition to the practical and system configuration challenges faced when moving from the laboratory to field studies, there are issues of experimental control and the characterization of Cognitive State in less constrained task environments. A key component of an evaluation is to compare the classification results with "ground truth," i.e. the actual workload experienced by the participant. While in the laboratory it is possible to develop simple tasks where workload is manipulated precisely and consistently, in an unconstrained field environment it becomes substantially harder to manipulate workload precisely and to interpret and assess a user's true Cognitive State without compromising operational realism. Advances in experimental design, data collection protocols, signal processing, classification, mitigation strategies, and experimental ground truth methodology successfully enabled classification of Cognitive workload level in an unconstrained, fully-mobile, free-play operation with Soldiers executing missions in a challenging urban terrain environment.
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An evaluation of real-time Cognitive State classification in a harsh operational environment
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2007Co-Authors: Michael C. Dorneich, Santosh Mathan, Patricia May Ververs, Stephen WhitlowAbstract:This paper describes an evaluation conducted with a full platoon of 32 Soldiers at Aberdeen Proving Grounds' MOUT site in Aberdeen, MD. The objective was to assess the Cognitive workload classification techniques driven by neuro-physiological (EEG) and physiological (ECG) sensors. In a first ever evaluation of real-time Cognitive monitoring in the harsh operational environment, the assessment culminated in a three phase, 24-hour mission consisting of a coordinated Route Reconnaissance, a Cordon and Search of a village, and a Hasty Defense operation. Task load levels were manipulated by introducing unexpected and unplanned events requiring re-planning and extensive coordination by the leadership (high task load) as well as lulls in the activity in which part missions were executed flawlessly with little variations on the preplanned, well versed drill (low task load). Four leaders (Platoon Leader, Platoon Sergeant, Squad Leader 1, and Squad Leader 2) were equipped with sensors to measure and output Cognitive State in real-time. The fused EEG and ECG workload classification approach reached 95% accuracy depending on the individual and the amount of data used to train the classifier. This level of success implies that Augmented Cognition workload assessment tools enable the ability to move beyond subjective workload rating scales, such as NASA TLX and Cooper Harper ratings, to more objective measurements of real-time Cognitive State metrics in almost any conceivable operational environment.
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Evaluation of a Tactile Navigation Cueing System and Real-Time Assessment of Cognitive State
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2006Co-Authors: Michael C. Dorneich, Patricia May Ververs, Stephen Whitlow, Santosh MathanAbstract:This paper details an evaluation of a tactile cueing system that was created to enhance the navigation of a complex route. The complexity of the task along with simultaneously challenging Cognitive tasks also enabled the real-time assessment of Cognitive State in various task load conditions. Honeywell has been working with the US Army's Future Force Warrior program to develop adaptive systems that will effectively manage the available Cognitive resources used for information processing by the dismounted Soldier in highly dynamic, information rich environments. The appropriate allocation of Cognitive resources is key to managing multiple tasks, focusing on the most important ones, and maintaining overall situation awareness. Non-visual navigation support would offload a typically visual task, such as viewing a paper map or computer-based map display, to a sensory channel that is underutilized, tactile sensation. Both benefits and costs to this type of automation support are explored in detail, where the evaluation supports the premise that strong automated support should only be used in high workload situations where the benefits outweigh the costs.
Hidekazu Yoshikawa - One of the best experts on this subject based on the ideXlab platform.
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Development of a real-time Cognitive State estimator
Control Engineering Practice, 1995Co-Authors: Makoto Takahashi, M. Kitamura, Hidekazu YoshikawaAbstract:Abstract A real-time Cognitive State estimator based on physiological data has been developed to provide information for a man-machine interface study. The system that had been developed for off-line estimation is first described, to outline the basic idea of Cognitive State estimation based on physiological data. The modification to develop a real-time system and to improve the estimation accuracy is then described as the phase-two study. The results of the numerical experiments showed that the developed system is capable of estimating a human's Cognitive State in real-time with sufficient accuracy.
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Development of Realtime Cognitive State Estimator
IFAC Proceedings Volumes, 1993Co-Authors: Makoto Takahashi, M. Kitamura, Hidekazu YoshikawaAbstract:Abstract The realtime Cognitive State estimator based on the physiological data is now under development to provide information for the Man-Machine Interface study. The system for off-line estimation that has already been developed is first described to outline our basic idea of Cognitive State estimation based on the physiological data. The modification to develop realtime system is then described in detail with the emphasis on the realtime data processing and data transfer methodology The current State of the system development is presented with the preliminary results of the realtime analysis.
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Estimation of Human Cognitive State by Psycho-Physiological Data
Transactions of the Institute of Systems Control and Information Engineers, 1992Co-Authors: Hidekazu Yoshikawa, Satoshi Kuroda, Takehiko Nishio, Masashi KitamuraAbstract:The estimation method for human Cognitive State at man-machine interface was investigated by means of psycho-physiological method. The estimation method is firstly to reduce data patterns seen in the short-time responses of multiple physiological measures, and then to construct discriminate functions which correlate the obtained data patterns with the different kinds of Cognitive State. The used physiological measures are eye saccade fraction, eye pupil size, eye blinking rate, skin potential response, heart rate and respiration rate. By using protocol data taken, from a subject during a, Cognitive task experiment, three different kinds of discrimination functions were constructed, based on either fuzzy logic method or statistical analysis (quantification method class ?2). The intercomparison of the three functions thus obtained was made with respect to the accuracy for the Cognitive State discrimination, along with the general discussion concerning the limitation of the proposed psycho-physiological method and its applicability to personal differences.
Jiayang Huang - One of the best experts on this subject based on the ideXlab platform.
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Cognitive State recognition using wavelet singular entropy and ARMA entropy with AFPA optimized GP classification
Neurocomputing, 2016Co-Authors: Zhengxiang Cai, Dan Huang, Lu Ding, Rob Law, Jiayang HuangAbstract:Cognitive State, which is the inner mental State of a person while interacting with an artificial system through man-machine interface, can be affected by various factors, such as fatigue, stress, mental workload, attention deficit, and executive function, among others, which can lead to errors, accidents, or even disasters. One practical solution to this problem is to monitor and recognize the Cognitive State of subjects via physiological signals. In this study, a hybrid adaptive flower pollination algorithm-Gaussian process model is proposed to recognize the Cognitive State of in-flight pilots. Instead of using the traditional conjugate gradient technique to find optimal hyperparameters, an improved flower pollination algorithm is proposed. The adaptive Levy strategy is then used to increase the robustness of this algorithm, as well as to enhance the global optimization and generalization capability of the Gaussian process model. In addition to conventional features in the time-frequency domain, a novel set of features involving wavelet singular entropy and autoregressive-moving average entropy is proposed to improve classification accuracy. Experiments are performed through flight simulations in a full flight simulator with six degrees of freedom. Comparable experimental results validate the feasibility of the proposed method for recognizing Cognitive State and provide a wide range of conclusions on the feature selection and feature patterns of Cognitive State.