In this study, a combined measure was developed based on various physiological indices in order to evaluate the mental workload during a dual task. To determine the mental effort required for each task, three physiological signals were recorded while ten subjects performed different versions of a dual task composed of tracking and mental arithmetic. These signals were the electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG), which were transformed into the suppression of alpha rhythm, eye blink interval, and heart rate variability (HRV), respectively. The alpha suppression provided proper information to infer the efforts for the arithmetic task, but not for the tracking task. Conversely, the blink interval and HRV permitted detailed inferences over the workload of the tracking task, but not for the arithmetic task. These results can be explained in terms of the multiple resources model of workload. The processing indexed by the alpha suppression is inferred to be different from that indexed by the blink interval or HRV. Finally, the physiological measures were combined into a single measure using different weight coefficients. The newly developed measure systematically increased with the difficulty of each task and significantly distinguished between the different versions of each task. Relevance to industry A combined measure of mental workload that has the ability to evaluate operators' mental effort in a multitask condition would be valuable in a natural working environment, because most such work is composed of multiple tasks. In this paper, an approach is described that developed a combined measure of mental workload based on three physiological indices.
- Factor analysis
- Mental workload
- Multiple regression analysis
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
- Safety, Risk, Reliability and Quality
- Human Factors and Ergonomics