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

Zhang, Jianhua; Peng, Xiao-Di; Liu, Hua; Raisch, Jörg; Wang, Rubin

doi:10.1007/s11571-013-9243-3

Cited by 17 publications

(6 citation statements)

References 43 publications

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“…for more information please refer to (Markos and Singh 2003b;Chandola et al 2009) survey papers. Zhang et al (2013a) proposed a method that extracted psychophysiological features to characterise the operators functional state (OFS), then used a Fuzzy c-mean (FSM) algorithm to classify the OFS. this approach is very promising if implemented in the context of detecting the unwanted OFS of a pilot during flight operations.…”

Section: Classification Approachesmentioning

confidence: 99%

A novel approach for pilot error detection using Dynamic Bayesian Networks

2014

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In the last decade Dynamic Bayesian Networks (DBNs) have become one type of the most attractive probabilistic modelling framework extensions of Bayesian Networks (BNs) for working under uncertainties from a temporal perspective. Despite this popularity not many researchers have attempted to study the use of these networks in anomaly detection or the implications of data anomalies on the outcome of such models. An abnormal change in the modelled environment's data at a given time, will cause a trailing chain effect on data of all related environment variables in current and consecutive time slices. Albeit this effect fades with time, it still can have an ill effect on the outcome of such models. In this paper we propose an algorithm for pilot error detection, using DBNs as the modelling framework for learning and detecting anomalous data. We base our experiments on the actions of an aircraft pilot, and a flight simulator is created for running the experiments. The proposed anomaly detection algorithm has achieved good results in detecting pilot errors and effects on the whole system.

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Section: Classification Approachesmentioning

confidence: 99%

A novel approach for pilot error detection using Dynamic Bayesian Networks

2014

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“…Compared with the former two approaches, the last approach is featured by continuous on-line measurement. ElectroEncephaloGram (EEG), ElectroCardioGram (ECG) and ElectroOculoGram (EOG) have been widely used for MWL recognition (Zhang et al 2013a , b , 2016 ; Wang et al 2018 ; Zeng et al 2018 ; Lamti et al 2019 ; Mora-Sánchez et al 2020 ). In this paper, we evaluate the operator MWL by using physiological signals and a semi-supervised learning technique in order to enhance the accuracy and efficiency of high-risk MWL detection.…”

Section: Introductionmentioning

confidence: 99%

Instantaneous mental workload assessment using time–frequency analysis and semi-supervised learning

2020

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The real-time assessment of mental workload (MWL) is critical for development of intelligent human–machine cooperative systems in various safety–critical applications. Although data-driven machine learning (ML) approach has shown promise in MWL recognition, there is still difficulty in acquiring a sufficient number of labeled data to train the ML models. This paper proposes a semi-supervised extreme learning machine (SS-ELM) algorithm for MWL pattern classification requiring only a small number of labeled data. The measured data analysis results show that the proposed SS-ELM paradigm can effectively improve the accuracy and efficiency of MWL classification and thus provide a competitive ML approach to utilizing a large number of unlabeled data which are available in many real-world applications.

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“…Compared with the former two approaches, the last approach is featured by continuous online measurement. ElectroEncephaloGram (EEG), ElectroCardioGram (ECG) and ElectroOculoGram (EOG) have been widely used for MWL recognition [6][7][8]. In this paper we evaluate the operators' MWL by using multi-modal psychophysiological signals and examine the potential and efficacy of semi-supervised learning technique for enhancing the accuracy and efficiency of high-risk MWL detection.…”

Section: Introductionmentioning

confidence: 99%

Instantaneous Mental Workload Classification Using Semi-Supervised Learning

Zhang

Lǐ

Nichele

2019

2019 IEEE Symposium Series on Computational Intelligence (SSCI)

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The real-time monitoring of human operator's mental workload (MWL) is crucial for development of adaptive/intelligent human-machine cooperative systems in various safety/mission-critical application fields. Although datadriven approach has shown promise in MWL recognition, its major difficulty lies in how to acquire sufficient labeled data to train the model. This paper applies semi-supervised extreme learning machine (SS-ELM) to the problem of MWL classification based only on a small number of labeled data. The experimental data analysis results have shown that the proposed SS-ELM paradigm can effectively improve the accuracy and efficiency of MWL classification. The proposed semi-supervised learning paradigm may provide an alternative data-driven machine learning approach to effectively utilize a large number of unlabeled data, which can be readily collected under naturalistic (operational) task environments in many real-world applications.

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

Cited by 17 publications

References 43 publications

A novel approach for pilot error detection using Dynamic Bayesian Networks

A novel approach for pilot error detection using Dynamic Bayesian Networks

Instantaneous mental workload assessment using time–frequency analysis and semi-supervised learning

Instantaneous Mental Workload Classification Using Semi-Supervised Learning

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