The progress of immersive technology enables researchers and developers to construct work spaces that are freed from real-world constraints. This has motivated us to investigate the role of the human body. In this research, we examine human cognitive behaviors in obtaining an understanding of the width of their virtual body through simple yet meaningful experiments using virtual reality (VR). In the experiments, participants were modeled as an invisible board, and a spherical object was thrown at the participants to provide information for exploring the width of their invisible body. Audio and visual feedback were provided when the object came into contact with the board (body). We first explored how precisely the participants perceived the virtual body width. Next, we examined how the body perception was generated and changed as the trial proceeded when the participants tried to move right or left actively for the avoidance of collision with approaching objects. The results of the experiments indicated that the participants could become successful in avoiding collision within a limited number of trials (14 at most) under the experimental conditions. It was also found that they postponed deciding how much they should move at the beginning and then started taking evasive action earlier as they become aware of the virtual body.

In this study, we achieved predictable control of a wheelchair by changing the existing mapping method of the joystick, which considers the consecutive operations of a motor of a wheelchair, to a new mapping method that corresponds to the internal model of a human being. Since the existing method uses the polar coordinate system, it is not easy at all to use this method to predict either the direction of motion or the operating order for changing the position of the wheelchair according to the requirements of an operator. In order to improve the embodiment, we divided the existing joystick mapping method into two degrees of freedom-one in the vertical axis that can control the velocity and the other, in the horizontal axis for direction control. Based on this division, we implemented a wheelchair model that can be controlled by the electromyography (EMG) signal from the neck and the arm muscles of an operator. This was achieved by mapping the divided degrees of freedom onto the degrees of freedom of the neck and arm of the operator. In this case, since the operator controls the direction of motion by the joint of his/her neck, he/she can move the wheelchair in the desired direction; thus, a more intuitive human interface is implemented.