In cold climates during the winter season, the road conditions deteriorate due to snowfall, causing pedestrians to fall over. This could lead to unexpected injury even if it is a single fall accident. Therefore, we have developed a shoe-type road surface identification device, considering that if we could mechanically determine the frozen road conditions, we could reduce the number of fall accidents. In this study, we reproduced the road surface of winter and tried to obtain a discriminant index. Healthy adults were targeted as subjects. Measured items included the amount of reflected light (This is the wavelength range that is easily absorbed by ice and water) and the temperature of road surface. As a result of having acquired light quantity and temperature in the swing phase using this device, a significant difference was found between dry and wet road surfaces. This suggested that the light quantity and the road surface temperature data might be suitable for road surface detection. In conclusion, this device might be useful to detect actual winter road surfaces.
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