Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices

Abstract

This research aims to identify a feasible model to predict a learner's stress in an online learning platform. It is desirable to produce a cost-effective, unobtrusive and objective method to measure a learner's emotions. The few signals produced by mouse and keyboard could enable such solutionto measure real world individual's affective states. It is also important to ensure that the measurement can be applied regardless the type of task carried out by the user. This preliminary research proposes a stress classification method using mouse and keystroke dynamics to classify the stress levels of 190 university students when performing three different e-learning activities. The results show that the stress measurement based on mouse and keystroke dynamics is consistent with the stress measurement according to the changes of duration spent between two consecutive questions. The feedforward back-propagation neural network achieves the best performance in the classification.

Cognitive Manufacturing in Industry 4.0 toward Cognitive Load Reduction: A Conceptual Framework

“…According to [19], the performance of a job is influenced by its nature, i.e., if the assignment requires the operators to use more or less cognitive power. Table 1 shows the causes of cognitive overload in manufacturing environments, according to the available literature.…”