Real-Time Assessment of Mental Workload Using Psychophysiological Measures and Artificial Neural Networks

By Glenn F. Wilson, Christopher A. Russell. Hum. Factors, 2003

404 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Social Psychology, Surgery

View Paper: https://www.semanticscholar.org/paper/e2ac5bc8b7b378fe3fa4414dba0c043c1d88729e

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Citations in the corpus

Emotion Assessment From Physiological Signals for Adaptation of Game Difficulty (2011, background): “Other works tried to infer operator engagement, fatigue, and workload by using EEG signals in order to adapt the complexity of a task [17]–[21].”
Performance Enhancement in an Uninhabited Air Vehicle Task Using Psychophysiologically Determined Adaptive Aiding (2007, methodology): “The validation data were also used to test the accuracy of the trained ANN (Wilson & Russell, 2003b).”
An empirical study of machine learning techniques for affect recognition in human–robot interaction (2005, methodology): “Artificial Neural Network has also been used to assess the mental workload and the mean classification accuracies were 85%, 82%, and 86% for the baseline, low task difficulty and high task difficulty conditions, respectively [12].”
Humans: Still Vital After All These Years of Automation (2008, background): “Wilson and Russell (2003) recorded several physiological variables (e.g., electroencephalograph, electrooculograph) during multitask performance and trained an artificial neural network (ANN) to differentiate states of rest and low and high workload.”
Removal of ocular artifacts from electro-encephalogram by adaptive filtering (2004, methodology): “F z , F 7 , P z , etc.) is used by a neural network to perform real-time classification of the pilot's functional state (WILSON and RUSSELL, 1999; 2003).”
Highly Automated Driving, Secondary Task Performance, and Driver State (2012, background): “Examples of such measures include (a) evoke-related potentials, (b) eye-tracking measures, including frequency, interval, and duration of blinks, and (c) heart-related measures, including heart rate and heart rate variability (Wilson & Russell, 2003).”
Objective measures, sensors and computational techniques for stress recognition and classification: A survey (2012, background): “models for emotion [128] and mental workload [129] (which have been based on physiological signals, e.”