In this study, we developed a new performance model that uses an index of difficulty to predict the pointing time required for a pointing task when using a PC mouse. Forty-nine subjects were classified into three age groups: young, middle-aged, and elderly. Experimental factors were the target size, the target distance, and the approach angle to the target. The time for pointing and the coordination of the pointer (mouse cursor) were measured in all conditions. The pointing time was significantly longer for the elderly group than for the young and middle-aged groups. Moreover, the pointing time in each age group tended to increase linearly with an increase in target distance and to decrease as a logarithmic function with an increase in target size. Based on these results, we proposed a new model for measuring performance when operating a PC mouse. Our model received a higher rating for ease-of-use than conventional models. It was clarified that the performance model for elderly persons was different from that for other age groups using the proposed model.

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 purpose of this study is to show the chaotic features of rhythmic joint movement. Depending on the experimental conditions, one (or both) elbow angle(s) was (were) measured by one (or two) goniometer(s). Pacing was provided for six different frequencies presented in random order. When the frequency of the pace increased, the fractal dimension and first Lyapunov exponent tended to increase. Moreover, the first Lyapunov exponent obtained positive values for all of the observed data. These results indicate that there is chaos in rhythmic joint movement and that the larger the frequency, the more chaotic the joint movement becomes.

In this study, we clarified the differences in the pointing time required when using a touch panel and a PC mouse for three age groups: young, middle-aged, and elderly. We constructed a performance model for a touch panel operation (Experiment 1). Moreover, we investigated the visual interference caused by a multi-target presentation (Experiment 2). The delay caused by visual interference for the right-hand target was longer than that for the left-hand target, and that for the upper target was longer than that for the lower target.

The purpose of this research is to show that the stress during the VDT task could be evaluated using the chaotic features for the focal accommodation system and the pupil area. The result of this experiment shows that the fractal dimension for the pupil area can be used to evaluate the stress during the VDT task. Moreover, it is shown that the chaotic property in the fixed target measurement is higher than that in the linear control and step control measurements. However, the first Lyapunov exponent hardly changed over time for all of three accommodation measurements.

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.