Blar i TKD - Institutt for informasjonsteknologi på emneord "Operator functional states"
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Classifying Mental Workload Levels Using Semi-Supervised Learning Technique
(IFAC-PapersOnLine;Volume 53, Issue 2, Peer reviewed; Journal article, 2021-04-14)Real-time monitoring and analysis 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 ... -
Instantaneous mental workload assessment using time–frequency analysis and semi-supervised learning
(Cognitive Neurodynamics;volume 14, issue 5, Peer reviewed; Journal article, 2020-05-12)The real-time assessment of mental workload (MWL) is critical for development of intelligent human–machine cooper- ative systems in various safety–critical applications. Although data-driven machine learning (ML) approach ... -
Instantaneous Mental Workload Recognition Using Wavelet-Packet Decomposition and Semi-Supervised Learning
(IEEE Symposium Series on Computational Intelligence (SSCI);2019 IEEE Symposium Series on Computational Intelligence (SSCI), Conference object, 2020-02-20)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 ... -
Operator Performance Prediction based on Fuzzy Modeling Approach
(IFAC-PapersOnLine;Volume 53, Issue 2, 2020, Peer reviewed; Journal article, 2021-04-14)In this paper, physiological signals were measured from five participants, each participating in two sessions of experiment with identical experimental procedure. A simulation platform, AutoCAMS (Automation-enhanced Cabin ... -
Physiological-signal-based mental workload estimation via transfer dynamical autoencoders in a deep learning framework
(Neurocomputing;Volume 347, 28 June 2019, Journal article; Peer reviewed, 2019-02-19)Evaluating operator mental workload (MW) in human-machine systems via neurophysiological signals is crucial for preventing unpredicted operator performance degradation. However, the feature of physiological signals is ...