Many researchers are interested in how the kinematics of reach-to-grasp movements in virtual reality (VR), which involve the vergence–accommodation conflict, differ from real space. The present study, conducted in VR, verified the effect of a discrepancy between visual and haptic target size and found that terminal haptic feedback is weighted more heavily than visual information, as is the case in real space. Furthermore, we investigated how the presence or absence of terminal haptic feedback modulates performance in VR by comparing prehension with or without terminal haptic feedback in VR, closed-loop natural grasping in real space, and pantomimed prehension with the eyes closed. Our results suggested that the contribution of a visual image to grip aperture adjustment is quite marginal in situations where terminal haptic feedback is unavailable, and that the performance in VR without haptic feedback is highly correlated with performances driven by fully internal representations (i.e., pantomimed movements).

We propose a novel path loss estimation method based on deep learning with some newly defined system parameters and images. Estimating the radio wave propagation environment is one of the key techniques for indoor/outdoor high-speed wireless communication. The radio wave propagation environment is basically a multipath environment, and path loss characteristics should be estimated under various environments. The authors have already proposed path loss estimation methods based on machine learning and spatial image data. The purpose of this paper is to further enhance the path loss estimation accuracy by appropriately selecting the input parameters and the CNN/FNN model structure.

We discuss how to extract building map images to be used in a learning-based method for predicting radio wave propagation. Learning-based prediction methods for radio wave propagation use building map images as spatial data, and we have already proposed a prediction method that uses images around Tx, Rx, and their midpoint. The method works well but has missing regions between Tx and Rx when the distance between them increases. In this paper, we further modify the method to include the whole area between Tx and Rx and also their surrounding regions in a single image. We present the method of using the dataset generated from measured data and an evaluation of how much the proposed method improves the prediction accuracy of radio wave propagation.

Most elderly people staying in nursing homes have considerable dementia, and various kinds of recreation program have hence been executed in order to improve or prevent this problem. However the effect of recreation has not been proved quantitatively. In this paper, we show the relationship between actions of elderly people with considerable dementia during recreation and abilities of memorization, emotion control, accommodation to society, etc., and decompose their actions into basic action factors. We propose a design method for recreation programs composed of effective factor actions for improving dementia. We analyze the effect of recreation in an experiment of robot therapy recreation based on the proposed method.