Real-Time Adaptive Automation System Based on Identification of Operator Functional State in Simulated Process Control Operations

Ting, Chou‐Chik; Mahfouf, Mahdi; Nassef, Ahmed M.; Linkens, D.A.; Panoutsos, George; Nickel, Peter; Roberts, Adam Charles; Hockey, G. R. J.

doi:10.1109/tsmca.2009.2035301

Cited by 85 publications

(57 citation statements)

References 28 publications

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“…Psychophysiological measures have been used in evaluating haptic robot interaction for stroke patients in a multi-modal virtual environment [120] and observed a weak psychophysiological response compared to healthy patients. Ting et al [121] proposed a framework of adaptive automation system based on the operator's mental state calculated through heart-rate variability and task load index. Munih and Mihelj presented a very interesting article that summarizes the psychophysiological response in robot-assisted rehabilitation including multi-modal challenges and physical activity [122].…”

Section: Human Robot Interaction (Hri) Studiesmentioning

confidence: 99%

A Survey on Psycho-Physiological Analysis & Measurement Methods in Multimodal Systems

Baig¹,

Kavakli²

2019

MTI

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Psycho-physiological analysis has gained greater attention in the last few decades in various fields including multimodal systems. Researchers use psychophysiological feedback devices such as skin conductance (SC), Electroencephalography (EEG) and Electrocardiography (ECG) to detect the affective states of the users during task performance. Psycho-physiological feedback has been successful in detection of the cognitive states of users in human-computer interaction (HCI). Recently, in game studies, psycho-physiological feedback has been used to capture the user experience and the effect of interaction on human psychology. This paper reviews several psycho-physiological, cognitive, and affective assessment studies and focuses on the use of psychophysiological signals in estimating the user's cognitive and emotional states in multimodal systems. In this paper, we review the measurement techniques and methods that have been used to record psycho-physiological signals as well as the cognitive and emotional states in a variety of conditions. The aim of this review is to conduct a detailed study to identify, describe and analyze the key psycho-physiological parameters that relate to different mental and emotional states in order to provide an insight into key approaches. Furthermore, the advantages and limitations of these approaches are also highlighted in this paper. The findings state that the classification accuracy of >90% has been achieved in classifying emotions with EEG signals. A strong correlation between self-reported data, HCI experience, and psychophysiological data has been observed in a wide range of domains including games, human-robot interaction, mobile interaction, and simulations. An increase in β and γ-band activity have been observed in high intense games and simulations.

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Section: Human Robot Interaction (Hri) Studiesmentioning

confidence: 99%

A Survey on Psycho-Physiological Analysis & Measurement Methods in Multimodal Systems

Baig¹,

Kavakli²

2019

MTI

View full text Add to dashboard Cite

show abstract

“…Following, the accuracy of this classifier should be verified by training it on a part of the data and performing a classification on another part of the data (Wilson & Russell, 2003). Next, real-time data acquisition and classification should be tested (Ting et al, 2010). If the classifier works properly, the prosthetic properties that might influence cognitive workload (i.e., timing or amount of assistance, .…”

Section: Cognitive Workload and Biocooperative Controlmentioning

confidence: 99%

Psychophysiological response to cognitive workload during symmetrical, asymmetrical and dual-task walking

Knaepen

Marušič

Crea

et al. 2015

Human Movement Science

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“…In particular, research into the effects of mental effort on various cardiovascular and respiratory measures has been very valuable for the development of operator state assessment techniques (Wilson and Russell, 2003;Kramer, 1991;Veltman and Gaillard, 1996;Sirevaag et al, 1993;Prinzel et al, 2000;Grossman and Taylor, 2007;Wientjes, 1992;Mulder and Mulder, 1981;Stuiver and Mulder, 2009) and promising results have been obtained that show the predictive power of such measures in relation to mental effort in the field of adaptive automation (Haarmann et al, 2009;Fairclough and Venables, 2006). Ting et al (2010), for example, presented a paper on real-time adaptive automation based on, amongst other measures, mid frequency heart rate variability data.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Cardiovascular Responses to Changing Task Demands

Stuiver

Waard

Brookhuis

et al. 2012

International Journal of Psychophysiology

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Real-Time Adaptive Automation System Based on Identification of Operator Functional State in Simulated Process Control Operations

Cited by 85 publications

References 28 publications

A Survey on Psycho-Physiological Analysis & Measurement Methods in Multimodal Systems

A Survey on Psycho-Physiological Analysis & Measurement Methods in Multimodal Systems

Psychophysiological response to cognitive workload during symmetrical, asymmetrical and dual-task walking

Short-Term Cardiovascular Responses to Changing Task Demands

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