For optimizing the balance between intellectual productivity and energy use in office buildings, we should reveal the mechanism of intellectual productivity variation of office workers. For this purpose, the authors had proposed an intellectual productivity model. The model is a state transit model based on short pauses while working. This model can explain the productivity variation of most of subject experiment results. In some cases, however, the explanation is insufficient with previous 2-state transit model. In this study, the authors have proposed an improved model assuming 3 states. A subject experiment was conducted where illuminance on the desk and work motivation were controlled to vary their productivity. The experimental result was emulated with 2-state/3-state transition models and their accuracies were compared. As a result it was confirmed that the 3-state model simulated more accurately. It means the 3-state transition model can explain productivity variation better than the 2-state transition model.
The authors have proposed a concentration time ratio (CTR) as a new evaluation index of intellectual productivity, which had been difficult to be quantitatively evaluated, with a concept of concentration on intellectual work.When quantitatively evaluating intellectual productivity with simple cognitive task method, the result is often affected by learning effect.In order to evaluate how office environment affects the CTR when an office environment is changed, a subject experiment was conducted in which the illumination condition of the room was changed.As the result, the performances of cognitive tasks were affected by the learning effect, however it was found that the index was affected not by the learning effect but by changing the illumination conditions.
In this study, an algorithm to detect temporary rest state when performing intelligent works by measuring of physiological indices has been developed. As the result of the experiment, the detecting performance was found 18.7% higher (p<0.01) than random detection rate. This result shows the possibility to use the physiological indices as one of the mental state detection methods.