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Vibration characteristics of a seated human body exposed to whole-body vibration are important factors in ride comfort of automobiles. In a frequency range below approximately 20 Hz, there are main resonance frequencies of the human body. Therefore many previous studies have discussed the vibration characteristics in this frequency range. However, it is necessary to focus on high-frequency vibration because recent development in vibration reduction technology of automobiles reduces low-frequency vibration and occupants can perceive high-frequency vibration. The purpose of this study is to examine the vibration characteristics of the seated human body in a frequency range below 50 Hz. For the purpose, vibration experiments for the human body with a shaking table were conducted. Subjects sat on a rigid seat fixed on the shaking table and were exposed to vertical vibration. Acceleration on the head and several positions on the spine was measured, and seat-to-measurement-point acceleration transmissibility was obtained. The modal characteristics were estimated from the measured data of the transmissibility by using multi-reference iterative curve fitting technique. Five modes were estimated below 50 Hz. The first, second, and third modes were in the frequency range below 20 Hz. The fourth and fifth modes were in a frequency range from 20 Hz to 50 Hz. The modal characteristics, which are a mode shape and a natural frequency, in the high-frequency range varied depending on the subjects, though the modal characteristics in the low-frequency range were almost the same regardless of the subjects.

Deep Learning image processing methods are gradually gaining popularity in a number of areas including medical imaging. Classification, segmentation, and denoising of images are some of the most demanded tasks. In this study, we aim at enhancing optic nerve head images obtained by Optical Coherence Tomography (OCT). However, instead of directly applying noise reduction techniques, we use multiple state-of-the-art image Super-Resolution (SR) methods. In SR, the low-resolution (LR) image is upsampled to match the size of the high-resolution (HR) image. With respect to image enhancement, the upsampled LR image can be considered as low quality, noisy image, and the HR image would be the desired enhanced version of it. We experimented with several image SR architectures, such as super-resolution Convolutional Neural Network (SRCNN), very deep Convolutional Network (VDSR), deeply recursive Convolutional Network (DRCN), and enhanced super-resolution Generative Adversarial Network (ESRGAN). Quantitatively, in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), the SRCNN, VDSR, and DRCN significantly improved the test images. Although the ERSGAN showed the worst PSNR and SSIM, qualitatively, it was the best one.

In this paper, we propose a new interaction technique for controlling applications by touching the forehead that the skin deformation is detected by an enhanced Head-Mounted Display (HMD). This technique envisions supporting commands and controls for applications as map applications and photo viewers without carrying additional controlling device which removes the user’s eye from the display. In this technique, the skin deformation is measured by attaching light sensors to the HMD. The deformation of the skin is caused by the user touching the forehead with the finger. In this paper, we support the recognition of multiple gestures with a Support Vector Machine (SVM).