Modeling the Operator Functional State for Emergency Response Management

New technologies are available for emergency management experts to help them cope with challenges such as information overload, multitasking and fatigue. Among these technologies, a wide variety of physiological sensors can now be deployed to measure the Operator Functional State (OFS). To be truly useful, such measures should not only characterize the overall OFS, but also the specific dimensions such as stress or mental workload. This experiment aimed to (1) design a multi-dimensional model of OFS, and (2) test its application to an emergency management situation. First, physiological data of participants were collected during controlled experimental tasks. Then, a support vector classifier of mental workload and stress was trained. Finally, the resulting model was tested during an emergency management simulation. Results suggest that the model could be applied to emergency management situations, and leave the door open for its application to emergency response on the field.

[1]  J. E. Korteling,et al.  Using neurophysiological signals that reflect cognitive or affective state: six recommendations to avoid common pitfalls , 2015, Front. Neurosci..

[2]  G. Borghini,et al.  Neuroscience and Biobehavioral Reviews , 2022 .

[3]  S. Hart,et al.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research , 1988 .

[4]  J. Miller,et al.  Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. , 1987, The American journal of cardiology.

[5]  Lauren Reinerman-Jones,et al.  Workload Is Multidimensional, Not Unitary: What Now? , 2015, HCI.

[6]  Qiang Ji,et al.  Cross-subject workload classification with a hierarchical Bayes model , 2012, NeuroImage.

[7]  Joel S. Warm,et al.  Vigilance Requires Hard Mental Work and Is Stressful , 2008, Hum. Factors.

[8]  Jean-Yves Dufour,et al.  Intelligent video surveillance systems , 2012 .

[9]  William J. Horrey,et al.  The challenges and opportunities of technological approaches to fatigue management. , 2011, Accident; analysis and prevention.

[10]  Jean-François Gagnon,et al.  Using SYnRGY to support design and validation studies of emergency management solutions , 2013, ISCRAM.

[11]  Gerhard Tröster,et al.  Discriminating Stress From Cognitive Load Using a Wearable EDA Device , 2010, IEEE Transactions on Information Technology in Biomedicine.

[12]  Simone Benedetto,et al.  Driver workload and eye blink duration , 2011 .

[13]  Scott M. Galster,et al.  Sense-Assess-Augment: A Taxonomy for Human Effectiveness , 2013 .

[14]  Alexander J. Casson,et al.  Artificial Neural Network classification of operator workload with an assessment of time variation and noise-enhancement to increase performance , 2014, Front. Neurosci..

[15]  Christopher Kennard,et al.  Eye Movements Discriminate Fatigue Due to Chronotypical Factors and Time Spent on Task – A Double Dissociation , 2014, PloS one.

[16]  Peter Nickel,et al.  Sensitivity and Diagnosticity of the 0.1-Hz Component of Heart Rate Variability as an Indicator of Mental Workload , 2003, Hum. Factors.

[17]  V. Vuksanović,et al.  Heart rate variability in mental stress aloud. , 2007, Medical engineering & physics.

[18]  G. Robert J. Hockey.  Operator functional state: the prediction of breakdown in human performance , 2005 .

[19]  A. Aubert,et al.  Heart Rate Variability in Athletes , 2003, Sports medicine.

[20]  Maarten A. Hogervorst,et al.  Combining and comparing EEG, peripheral physiology and eye-related measures for the assessment of mental workload , 2014, Front. Neurosci..

[21]  M. N. Sawka,et al.  Operator Functional State Assessment (l'evaluation de i'aptitude operationnelle de i'operateur humain) , 2004 .