Signal processing techniques for natural sleep posture estimation using depth data

Several efforts are being made in studying sleeping posture of a person under natural conditions without causing discomfort. In this study, various methods are proposed and explored in which features defining sleep postures of a person are investigated for intelligent pattern recognition. Using the measured depth data, three dimensional depth scans as well as the cross-sectional scans of the static sleeping body are captured. 1D Fast Fourier Transforms are used as complementary feature of the cross-sectional scans in pattern recognition. Due to better orientation-frequency characteristics, the two dimensional Gabor filter banks are also applied on depth images to produce Gabor images from which features known as Gabor features can be extracted. These selected features can be further integrated as a part of natural sleep posture recognition.

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