The new generation of watches is smart. Smart watches are connected to the internet and provide sensor functionality that allows an enhanced human-computer-interaction. Smart watches provide a gesture interaction and a permanent monitoring of physical activities. In comparison to other electronic home consumer devices with integrated sensors, Smart watches provide monitoring data for 24h per day, many watches are water resistant and can be worn constantly. The integrated sensors are varying in performance and are not intended to distinguish between different states of activity and inactivity. This paper reports on identified requirements on sensors of smart watches for detection of activity, inactivity as well as sleep detection. Hereby a new measurement quantity is introduced and applications of heart beat detection or wearing situation are presented
Color cast is a crucial problem for color image processing. White balance has been widely used to eliminate color cast to improve the image's quality. Most of white balance implementations are based on color constancy hypothesis. A wellknown color constancy hypothesis is given in [1], unifying White Patch [2], Grey World [3], Shades of Grey [4], and Grey Edge [1] assumptions in one expression. However, this general hypothesis works on underwater images not as reliable as on common images. In the color constancy hypothesis for common scenes, the ambient light source is spatial constant. But in underwater scenes, the light suffers from serious attenuation, especially in the red part of the visible spectrum. This attenuation causes spatial variance of the ambient light source, which lets classic color constancy hypothesis fail. In this paper, we propose a novel low-level image feature-based color constancy hypothesis for underwater scenes. Based on this hypothesis, we propose an algorithm, using a distance map to estimate multiple gain factors to remove the color cast
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