Haptic perception is one of the primary means of interaction with the world. Recent research on affective haptics suggests that it can affect emotional and behavioral responses. In this study, we evaluate user perceptions of haptic stimuli generated by air vortex rings on the cheek and investigate the effects on their physiological responses. To develop a cheek haptic display, we investigated and found that the cheek had enough resolution to perceive the differences in haptic stimuli in a two-point discrimination threshold test of the face. Additionally, the intensities of the haptic stimuli for experiments were determined by investigating the subjective impressions of different stimuli pairs. Finally, we conducted experiments to evaluate quantitatively the effects of four different combinations of haptic stimuli on the physiological responses in terms of stress modification, brainwave activities, task performance, and subjective assessment. The results suggest that different stimuli affect physiological responses and task performance.

Auroras are beautiful phenomena and attract many people. However, its physical model still remains a subject of dispute because it is caused by the interaction of diverse areas, such as solar wind, magnetosphere, and ionosphere, and it is difficult to simultaneously obtain data in such wide areas. This paper is devoted to forecasting the onset of brightening of auroras followed by poleward expansion, called auroral substorms. We adopt a data-driven approach, instead of physical models of auroras. This approach requires labeled data, which shows when auroras appeared. However, this is challenging because there exist a wide variety of observed data from diverse areas while they are not tied with onset time of auroras. We identified auroral substorms using all-sky images obtained at Tromso, Norway. Then, we chose solar wind and geomagnetic field data as the first attempt toward the goal, out of many types of data, and associated them with the onset times of the identified auroral substorms. We trained a classifier of the support vector machine, which is a typical supervised learning algorithm, using the constructed data, and the classifier achieves around 78% classification accuracy at 5-fold cross validation.