Hiroyuki Okuda

Modeling of Human Behavior in Man-Machine Cooperative System Based on Hybrid System Framework

Recently, the demand for a man-machine cooperative system, where the machine assists the human operator, is rapidly growing in the industrial fields. To meet this demand, the human model is required to design the suitable assist controller in the man-machine cooperative system. This paper presents a new human behavior model based on a piece-wise affine model which is a class of hybrid dynamical system, and apply it to a sliding task. Since the human behavior is considered to consist of several primitive motions expressed by continuous dynamics and a decision-making expressed by the discrete switch, it seems to be natural to introduce the hybrid system modeling. Particularly, the decision strategy for the number of discrete modes is addressed by using a hierarchical clustering technique, and the measured data are classified into several modes. Then, each primitive motion in each mode is identified based on the affine model. Finally, the switching conditions among modes are identified by applying support vector machine to the classified data. The obtained piece-wise affine model can quantitatively represent both primitive motions and decision-making in the human behavior

2A2-M11 Measurement and Analysis of EEG in Driving Situation on Simulator

Recently, the analysis and application of biosignal is attracting great attention. In particular, the estimation of human internal state based on the electroencephalogram (EEG) signal is a promising strategy because of its huge amount of information. In this paper, first of all, the EEG signal under the driving situation is captured synchronizing with the behavioral signals such as the pedal operation. Then, the ‘mode’ underlying the observed EEG signal is extracted by applying the stochastic switched autoregressive (SS-AR) model. Finally, the meaning of each mode is investigated from viewpoint of the correspondence with the driver’s internal state.