Researchers have already attributed a certain amount of variability and “drift” in an individual's handwriting pattern to mental workload, but this phenomenon has not been explored adequately. Especially, there still lacks an automated method for accurately predicting mental workload using handwriting features. To solve the problem, we first conducted an experiment to collect handwriting data under different mental workload conditions. Then, a predictive model (called SVM-GA) on two-level handwriting features (i.e., sentence- and stroke-level) was created by combining support vector machines and genetic algorithms. The results show that (1) the SVM-GA model can differentiate three mental workload conditions with accuracy of 87.36% and 82.34% for the child and adult data sets, respectively and (2) children demonstrate different changes in handwriting features from adults when experiencing mental workload.

Ship handling for leaving and entering port always carries out for a captain, deck officers and quartermasters and sometimes include a pilot. For navigational watch keeping at sea except for a narrow channel and under restricted visibility etc., the deck officer and quartermaster do it. They achieve safe and efficient navigational watch keeping with their teamwork at a ship's bridge. The importance of teamwork has been recognized in the shipping world, and its training and education methods are also thought over. However, their evaluation is not clear, because they are depended on the experience of the trainers. Therefore, we need to make an evaluation method of teamwork for education and training of the ship handling. In this paper, we define that ship's bridge teamwork is shown by 1) a change of mental workload level and 2) a change of mental workload for time. We challenge to evaluate teammates' mental workload in the ship's bridge with R-R interval of subjects' heart rate variability, and we evaluate their mental workloads with the following three steps. 1) To confirm the evaluation of the mental workload of a ship's navigator with R-R interval. 2) To evaluate teamwork with R-R interval in case of an oral presentation at meetings as pre-experiments. 3) To evaluate the teammates' mental workload among ship's bridge team in case of a leaving port. Their results showed that the method using R-R interval was sufficient for the evaluation of teamwork effects.

In this paper, an attempt was made to evaluate mental workload using chaotic analysis of EEG. EEG signals registered from Fz and Cz during a mental task (mental addition) were recorded and analyzed using attractor plots, fractal dimensions, and Lyapunov exponents in order to clarify chaotic dynamics and to investigate whether mental workload can be assessed using these chaotic measures. The largest Lyapunov exponent for all experimental conditions took positive values, which indicated chaotic dynamics in the EEG signals. However, we could not evaluate mental workload using the largest Lyapunov exponent or attractor plot. The fractal dimension, on the other hand, tended to increase with the work level. We concluded that the fractal dimension might be used to evaluate a mental state, especially a mental workload induced by mental task loading.

It is generally known that the autonomic nervous system regulates the pupil. In this study, we attempted to assess mental workload on the basis of the fluctuation rhythm in the pupil area. Controlling the respiration interval, we measured the pupil area during mental tasking for one minute. We simultaneously measured the respiration curve to monitor the respiration interval. We required the subject to perform two mental tasks. One was a mathematical division task, the difficulty of which was set to two, three, four, and five dividends. The other was a Sternberg memory search task, which had four work levels defined by the number of memory sets. In the Sternberg memory search, the number of memory set changed from five to eight. In such a way, we changed the mental workload induced by mental loading. As a result of calculating an autoregressive (AR) power spectrum, we could observe two peaks which corresponded to the blood pressure variation and respiratory sinus arrhythmia under a low workload. With an increased workload, the spectral peak related to the respiratory sinus arrhythmia disappeared. The ratio of the power at the low frequency band, from 0.05-0.15Hz, to the power at the respiration frequency band, from 0.35-0.4Hz, increased with the work level. In conclusion, the fluctuation of the pupil area is a promising means for the evaluation of mental workload or autonomic nervous function.

The aim of this study is to evaluate mental workload (MWL) quantitatively by HRV (Heart Rate Variability) measures. The electrocardiography and the respiration curve were recorded in five different epochs (1) during a rest condition and (2) during mental arithmetic tasks (addition). In the experiment, subjects added two numbers. The work levels (figures of the number in the addition) were set to one figure, two figures, three figures and four figures. The work level had effects on the mean percent correct, the number of answers and the mean processing time. The psychological evaluation on mental workload obtained by the method of paired comparison increased with the work level. Among the statistical HRV measures, the number of peak and trough waves could distinguish between the rest and the mental loading. However, mental workload for each work level was not evaluated quantitatively by the measure. The HRV measures were also calculated from the power spectrum estimated by the autoregressive (AR) model identification. The ratio of the low frequency power to the high frequency power increased linearly with the work level. In conclusion, the HRV measures obtained by the AR power spectrum analysis were found to be sensitive to changes of mental workload.