Abstract
One goal of advanced information and communication technology is to simplify work. However, there is growing consensus regarding the negative consequences of inappropriate workload on employee's health and the safety of persons. In order to develop a method for continuous mental workload monitoring, we implemented a task battery consisting of cognitive tasks with diverse levels of complexity and difficulty. We conducted experiments and registered the electroencephalogram (EEG), performance data, and the NASA-TLX questionnaire from 54 people. Analysis of the EEG spectra demonstrates an increase of the frontal theta band power and a decrease of the parietal alpha band power, both under increasing task difficulty level. Based on these findings we implemented a new method for monitoring mental workload, the so-called Dual Frequency Head Maps (DFHM) that are classified by support vectors machines (SVMs) in three different workload levels. The results are in accordance with the expected difficulty levels arising from the requirements of the tasks on the executive functions. Furthermore, this article includes an empirical validation of the new method on a secondary subset with new subjects and one additional new task without any adjustment of the classifiers. Hence, the main advantage of the proposed method compared with the existing solutions is that it provides an automatic, continuous classification of the mental workload state without any need for retraining the classifier—neither for new subjects nor for new tasks. The continuous workload monitoring can help ensure good working conditions, maintain a good level of performance, and simultaneously preserve a good state of health.
Highlights
Advanced information and communication technology, highly interactive work environments, and work assistance systems impose increasingly high demands on our cognitive capacity and on the ability to cope with mental workload
We developed a new method for continuous mental workload classification based on the so-called Dual Frequency Head Maps (DFHM)
In order to evaluate the new proposed DFHM method, in the following we consider the obtained results and discuss them in comparison with the tendencies derived from the subjective ratings and the performance measurements
Summary
Advanced information and communication technology, highly interactive work environments, and work assistance systems impose increasingly high demands on our cognitive capacity and on the ability to cope with mental workload. One main goal of information and communication technology is to simplify work, there is growing consensus concerning the negative consequences of inappropriate workload on human’s health and the safety of persons. Under optimized work conditions we await performance at its best whilst simultaneously preserve employee’s health. In this context, a reliable and objective method for capturing mental workload continuously is absolutely essential. The long-term goal is to develop a method to be able to recognize critical states like high and low workload
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