Shogo Yamashita

We propose a potential fluid-measurement technology aimed at supporting biomechanics research of water sports using fluid simulation and motion analysis. Cellulose nanofibers introduced into the water as tracer particles to visualize the movement of water. An optical property of nanofibers, called flow birefringence, makes water flows brighter than their surroundings when placed between right and left circularly polarized plates. We tested the capability of the technology in a water tank and succeeded in using an existing particle-tracking method-particle image velocimetry (PIV)-to measure the flows from a pump in the tank.

Understanding water flow around a swimmer is key to reducing the water resistance when swimming and improving propulsive force, since swimmers who produce less turbulent flows around their bodies can, for example, swim faster. However, existing water flow measurement technologies are not suitable for measuring human swimmers because they can only measure a limited area and have potential adverse health effects on swimmers. In this research, we propose a harmless method of water flow measurement using food-grade particles and a harmless light source. To visualize the movement of water, cellulose particles (microcrystalline cellulose) are introduced into the water. We use an optical property of cellulose particles, called birefringence, which becomes brighter than its surroundings when placed between right and left circularly polarized plates and which enables flow measurement by using an existing particle tracking method. The proposed water flow measuring technology aims mainly at enhancing swimming training. However, this technology could also contribute to the advancement of human-computer interaction underwater and to education on basic fluid dynamics more generally. To test the capabilities of the proposed water flow measurement technology for measuring slight water flows, such as turbulent flows, we prepared objects of various shapes, including a spherical object (high resistance), streamlined objects (low resistance), and a human-shaped doll posed in several swimming forms, and put them into a water tank with a steady water flow. Expected water flows were observed and measured using the proposed flow measurement technology.

Water flow is strongly related to swimming speed; thus, technologies for measuring the three-dimensional movement of water are highly desired in the water sports industry. However, existing fluid measurement methods are not suitable for use with humans because they introduce tiny plastic particles (known as tracer particles) that contain fluorescent ink, which is used to visualize water flow. A laser then irradiates the environment to make the particles brighter than the surroundings to track their movement with cameras. This method has potential adverse effects to humans, such as accidental swallowing of the particles and laser burns to the skin and eyes. In this research, we propose a human-friendly water flow measuring technology using tracer particles made of food-grade materials and a harmless light source. To visualize tracer particles, we give the particles an optical property, which makes them sufficiently brighter than the surroundings when placed between circularly polarized plates. We tested the proposed setup for water flow measurement in an actual swimming environment with swimmers. We observed that tracer particles moved in accordance with the water flow caused by a swimming stroke.

AquaCAVE is a system for enhancing the swimming experience. Although swimming is considered to be one of the best exercises to maintain our health, swimming in a pool is normally monotonous; thus, maintaining its motivation is sometimes difficult. AquaCAVE is a computer-augmented swimming pool with rear-projection acrylic walls that surround a swimmer, providing a CAVE-like immersive stereoscopic projection environment. The swimmer wears goggles with liquid-crystal display (LCD) shutter glasses, and a pair of infrared cameras installed above the pool tracks the swimmer’s head position. Swimmers can be immersed into synthetic scenes, such as coral reefs, outer space, or any other computer generated environments. The system can also provide swimming training with projections such as record lines or swimming forms.

EyeSeWe propose, EyeSee a framework to help users understand what invisible phenomenon such as radiation and electricity are without the exclusive knowledge. EyeSee achieves this by projecting the sensor values into the actual measurement points. This visualization on the real environment is quite useful to demonstrate how these work. For example, people know radiation is a phenomenon, which we need avoid to be exposed, but how it distributes is not well known in general. In addition to this, people tend to feel too much fear because it is invisible and unfamiliar. Visualization on real environment can remove this issue by making invisible phenomenon not special for people. For instance, if you can directly see radiation on the real environment, they can put shielding materials near by the radioactive source and see how radiation can be shielded indeed just like shielding visible light from fluorescent lamp in the room. They can also know what exactly different from normal light in a practical manner.

We present POVeye, a method to help users in capturing and creating visualization of products for extensive representation of the product’s material color and texture. POVeye achieve this by providing realistic images captured from various angles, which are positioned correctly based on the calculated geometrical centroid. As input, users simply provide a video or multiple images of the product taken by any camera from arbitrary angles, without requiring any pre-calibration. POVeye provides an interface that shows object-centric camera positions alongside with image taken from respective camera angle. Users are able to either manually browse through automatically detected camera positions, or visualize the product by automatically detected view-angle path. POVeye leverages Structure-from-Motion (SfM) approach to obtain camera-object map. Our approach is unique from other solutions by preserving realistic imaging condition. We observe that visualization of products from different angles that provide information of light reflection and refraction potentially helps users to identify materials, and further perceive quality of a product.

We present Palette, a method to objectively quantize material color and airiness to provide representative description of a product in online shopping scenarios. Photos and keywords are often used to describe color, texture, and airiness of products. However consumer photos are usually taken under uncontrolled realistic imaging conditions, whereas keywords are fuzzy and highly subjective. Palette leverages active spectrophotometry approach that involves synchronized illumination to measure the reflection and transmission properties of a material as a function of wavelength. We use a Charge-Coupled Device (CCD) sensor equipped camera to capture visible light and near-infrared light intensity. We show that by analyzing the obtained light spectrum, we are able to provide a metric to represent material color and airiness. In this paper, we describe the details in principle of operation and proof-of-concept prototype implementation, as well as reporting results of our analysis using 4 types of garments. To the best of our knowledge, Palette is the first work to exploit spectrophotometry to represent garment color, texture, and airiness; as an effort to enrich user experience in online shopping.