Abstract Psychophysiological state prediction is of great importance to the human-machine interface (HMI) as far as both safety and reliability are concerned. In this paper, the use of facial temperature changes for predicting psychophysiological state and task performance has been investigated. The effectiveness of using facial temperature with the thermal camera to estimate the human psychophysiological state has been validated with the statistical results from a carefully designed HMI experiment with ten (10) healthy subjects. The new facial temperature biomarkers have exhibited a similar or even better ability to differentiate various psychophysiological state in comparison with the traditional biomarkers (e.g. heart rate variability (HRV), task load index (TLI) and pupil size). The mean nasal temperature and the differential energy between philtrum and forehead (DEFP) have been shown to be more sensitive to the psychophysiological state changes comparing to the conventional biomarkers. The maximum facial temperature and the mean forehead temperature have also shown clear correlations with psychophysiological state and task performance. As a final step, an adaptive type-2 fuzzy logic model combining the new markers with previously used bio-indicators has elicited and as a result achieved significantly high accuracy in predicting task load performance.


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    Titel :

    Thermal Imaging for Psychophysiological State Detection in the Human-Machine Interface (HMI) Control System


    Beteiligte:


    Erscheinungsdatum :

    2019-10-26


    Format / Umfang :

    16 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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