A new research has been published in International Journal of Computer Vision about analysis of sleep positions using computer vision and suggesting appropriate bed types. CVC, Dormity.com and S.U.R.F collaborated together in the research.
The research proposes an automatic sleep system recommendation using RGB, depth and pressure information. It consists of a validated clinical knowledge based model that, along with a set of prescription variables extracted automatically, obtains a personalized bed design recommendation. The automatic process starts by performing multi-part human body RGB-D segmentation combining GrabCut, 3D Shape Context descriptor and Thin Plate Splines, to then extract a set of anthropometric landmark points by applying orthogonal plates to the segmented human body. The extracted variables are introduced to the computerized clinical model to calculate body circumferences, weight, morphotype and Body Mass Index categorization. Furthermore, pressure image analysis is performed to extract pressure values and at-risk points, which are also introduced to the model to eventually obtain the final prescription of mattress, topper, and pillow. The complete system was validated in a set of 200 subjects showing accurate category classification and high correlation results with respect to manual measures.
The paper was signed by former CVC Researchers Christina Palmero, Pouya Ahmadmonfared and current CVC Researcher Sergio Escalera along with the collaboration of Gimbernat University Research Service on Physiotherapy (S.U.R.F) (Jordi Esquirol, Vanessa Bayo, Miguel Àngel Cos) and Dormity.com (Joan Salabert and David Sánchez)
Click to read the full paper in International Journal of Computer Vision
