Fuzzy Logic based Identification of Operator Functional States Using Multiple Physiological and Performance Measures

Zhang, Jianhua; Wang, Xingyu; Mahfouf, Mahdi; Linkens, D.A.

doi:10.1109/bmei.2008.181

Cited by 7 publications

(12 citation statements)

References 8 publications

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“…There are few studies employing NFS for MWL estimation. In studies by Zhang et al and Ting et al [53,58,59], a Genetic Algorithm (GA)-based Mamdani fuzzy model was used to optimize the FIS parameters. Here, 7.5 min intervals were used, with features from an EEG and cardiac sensor.…”

Section: Multimodal Fusion For Inferring Mwlmentioning

confidence: 99%

“…An extension of this work was further presented in a study conducted by Wang et al [46], with 2 min intervals, using Differential Evolution (DE) and Differential Evolution Algorithm with Ant Colony Search (DEACS) as means to optimize the ANFIS parameters. Zhang et al and Wang et al [46,58,59] demonstrated good results in an offline calibration and validation with the aforementioned models. Nevertheless, they also implemented a conventional ANFIS model for comparison, which showed poor results.…”

Section: Multimodal Fusion For Inferring Mwlmentioning

confidence: 99%

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Online Multimodal Inference of Mental Workload for Cognitive Human Machine Systems

et al. 2021

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With increasingly higher levels of automation in aerospace decision support systems, it is imperative that the human operator maintains a high level of situational awareness in different operational conditions and a central role in the decision-making process. While current aerospace systems and interfaces are limited in their adaptability, a Cognitive Human Machine System (CHMS) aims to perform dynamic, real-time system adaptation by estimating the cognitive states of the human operator. Nevertheless, to reliably drive system adaptation of current and emerging aerospace systems, there is a need to accurately and repeatably estimate cognitive states, particularly for Mental Workload (MWL), in real-time. As part of this study, two sessions were performed during a Multi-Attribute Task Battery (MATB) scenario, including a session for offline calibration and validation and a session for online validation of eleven multimodal inference models of MWL. The multimodal inference model implemented included an Adaptive Neuro Fuzzy Inference System (ANFIS), which was used in different configurations to fuse data from an Electroencephalogram (EEG) model’s output, four eye activity features and a control input feature. The results from the online validation of the ANFIS models demonstrated that five of the ANFIS models (containing different feature combinations of eye activity and control input features) all demonstrated good results, while the best performing model (containing all four eye activity features and the control input feature) showed an average Mean Absolute Error (MAE) = 0.67 ± 0.18 and Correlation Coefficient (CC) = 0.71 ± 0.15. The remaining six ANFIS models included data from the EEG model’s output, which had an offset discrepancy. This resulted in an equivalent offset for the online multimodal fusion. Nonetheless, the efficacy of these ANFIS models could be seen with the pairwise correlation with the task level, where one model demonstrated a CC = 0.77 ± 0.06, which was the highest among all the ANFIS models tested. Hence, this study demonstrates the ability for online multimodal fusion from features extracted from EEG signals, eye activity and control inputs to produce an accurate and repeatable inference of MWL.

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Section: Multimodal Fusion For Inferring Mwlmentioning

confidence: 99%

Section: Multimodal Fusion For Inferring Mwlmentioning

confidence: 99%

Online Multimodal Inference of Mental Workload for Cognitive Human Machine Systems

et al. 2021

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“…Серед найбільш поширених методів, які дозволяють проводити оцінку ФС операторів, можна виділити методи визначення обсягу короткочасної пам'яті, швидкості сприйняття інформації, інтенсивності уваги, точності і тривалості відтворення інтервалів часу, лабільності нервової системи [1], а також методи визначення значень психофізіологічних характеристик оператора, оцінки якості діяльності та надійності системи, показників активності підсистем організму людини (за даними вимірювання температури, електропровідності біологічно активних точок (БАТ), електрофізіологічних показників (ЕКГ, ЕМГ та ін.)) [2][3][4].…”

Section: аналіз літературних даних і постановка проблемиunclassified

Development and use of information technology for evaluating an operator’s visual profile functional state

Saikivska¹

2015

TAPR

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“…If HRV 1 is to be effective as a marker of mental effort, stress or strain, it is expected that it shows a progressive reduction with the increment of workload. Here the HR is taken as the average of heart rate every minute, while HRV 2 is defined as the ratio between the standard deviation and mean value of HR data segment of 1 min (Wilson 1999;Zhang et al 2008b):…”

Section: Ofs Feature Extractionmentioning

confidence: 99%

Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method

et al. 2013

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The human operator's ability to perform their tasks can fluctuate over time. Because the cognitive demands of the task can also vary it is possible that the capabilities of the operator are not sufficient to satisfy the job demands. This can lead to serious errors when the operator is overwhelmed by the task demands. Psychophysiological measures, such as heart rate and brain activity, can be used to monitor operator cognitive workload. In this paper, the most influential psychophysiological measures are extracted to characterize Operator Functional State (OFS) in automated tasks under a complex form of human-automation interaction. The fuzzy c-mean (FCM) algorithm is used and tested for its OFS classification performance. The results obtained have shown the feasibility and effectiveness of the FCM algorithm as well as the utility of the selected input features for OFS classification. Besides being able to cope with nonlinearity and fuzzy uncertainty in the psychophysiological data it can provide information about the relative importance of the input features as well as the confidence estimate of the classification results. The OFS pattern classification method developed can be incorporated into an adaptive aiding system in order to enhance the overall performance of a large class of safety-critical human-machine cooperative systems.

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Fuzzy Logic based Identification of Operator Functional States Using Multiple Physiological and Performance Measures

Cited by 7 publications

References 8 publications

Online Multimodal Inference of Mental Workload for Cognitive Human Machine Systems

Online Multimodal Inference of Mental Workload for Cognitive Human Machine Systems

Development and use of information technology for evaluating an operator’s visual profile functional state

Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method

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