TY  - JOUR
AU  - Cristina Palmero
AU  - Jordi Esquirol
AU  - Vanessa Bayo
AU  - Miquel Angel Cos
AU  - Pouya Ahmadmonfared
AU  - Joan Salabert
AU  - David Sanchez
AU  - Sergio Escalera
PY  - 2017//
TI  - Automatic Sleep System Recommendation by Multi-modal RBG-Depth-Pressure Anthropometric Analysis
T2  - IJCV
JO  - International Journal of Computer Vision
SP  - 212–227
VL  - 122
IS  - 2
KW  - Sleep system recommendation
KW  - RGB-Depth data Pressure imaging
KW  - Anthropometric landmark extraction
KW  - Multi-part human body segmentation
N2  - This paper presents a novel system for 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. We validate the complete system in a set of 200 subjects, showing accurate category classification and high correlation results with respect to manual measures.
L1  - http://refbase.cvc.uab.es/files/PEB2016.pdf
UR  - http://dx.doi.org/10.1007/s11263-016-0919-0
N1  - HuPBA;MILAB; 303.100
ID  - Cristina Palmero2017
ER  -