Automatic digital biometry analysis based on depth maps

World Health Organization estimates that 80% of the world population is affected by back-related disorders during his life. Current practices to analyze musculo-skeletal disorders (MSDs) are expensive, subjective, and invasive. In this work, we propose a tool for static body posture analysis and dynamic range of movement estimation of the skeleton joints based on 3D anthropometric information from multi-modal data. Given a set of keypoints, RGB and depth data are aligned, depth surface is reconstructed, keypoints are matched, and accurate measurements about posture and spinal curvature are computed. Given a set of joints, range of movement measurements is also obtained. Moreover, gesture recognition based on joint movements is performed to look for the correctness in the development of physical exercises. The system shows high precision and reliable measurements, being useful for posture reeducation purposes to prevent MSDs, as well as tracking the posture evolution of patients in rehabilitation treatments.

[1]  Pietro Garofalo,et al.  First in vivo assessment of “Outwalk”: a novel protocol for clinical gait analysis based on inertial and magnetic sensors , 2009, Medical & Biological Engineering & Computing.

[2]  Sarnadskiy Vn Classification of postural disorders and spinal deformities in the three dimensions according to computer optical topography. , 2012 .

[3]  Denis Gravel,et al.  Reliability of a quantitative clinical posture assessment tool among persons with idiopathic scoliosis. , 2012, Physiotherapy.

[4]  Sander Oude Elberink,et al.  Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications , 2012, Sensors.

[5]  Marc Parizeau,et al.  A Comparative Analysis of Regional Correlation, Dynamic Time Warping, and Skeletal Tree Matching for Signature Verification , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  B Drerup,et al.  Back shape measurement using video rasterstereography and three-dimensional reconstruction of spinal shape. , 1994, Clinical biomechanics.

[7]  Nelson G. Durdle,et al.  Clinical monitoring of torso deformities in scoliosis using structured splines models , 2008, Medical & Biological Engineering & Computing.

[8]  Mauro R. Ruggeri,et al.  Automatic scan registration using 3D linear and planar features , 2010 .

[9]  S. Adamovich,et al.  Virtual reality-augmented rehabilitation for patients following stroke. , 2002, Physical therapy.

[10]  Deyi Xue,et al.  Prediction of scoliosis progression with serial three-dimensional spinal curves and the artificial progression surface technique , 2010, Medical & Biological Engineering & Computing.

[11]  Richard K. Beatson,et al.  Reconstruction and representation of 3D objects with radial basis functions , 2001, SIGGRAPH.

[12]  Mohan M. Trivedi,et al.  Articulated body posture estimation from multi-camera voxel data , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[13]  Paul McCrory,et al.  Validity and reliability of the Nintendo Wii Balance Board for assessment of standing balance. , 2010, Gait & posture.

[14]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Andrew W. Fitzgibbon,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR 2011.

[16]  Nelson G. Durdle,et al.  Characterizing Torso Shape Deformity in Scoliosis Using Structured Splines Models , 2009, IEEE Transactions on Biomedical Engineering.

[17]  Pavel Senin,et al.  Dynamic Time Warping Algorithm Review , 2008 .

[18]  Carl-Fredrik Westin,et al.  Robust Generalized Total Least Squares Iterative Closest Point Registration , 2004, MICCAI.

[19]  Jake K. Aggarwal,et al.  Segmentation of 3D range images using pyramidal data structures , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[20]  Jitendra Malik,et al.  Efficient shape matching using shape contexts , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Farida Cheriet,et al.  Validity of a Quantitative Clinical Measurement Tool of Trunk Posture in Idiopathic Scoliosis , 2010, Spine.

[22]  Michael Potmesil,et al.  A lens and aperture camera model for synthetic image generation , 1981, SIGGRAPH '81.

[23]  Xujia Qin,et al.  An Image Inpainting Algorithm Based on CSRBF Interpolation , 2006 .

[24]  Nelson G. Durdle,et al.  Validating an imaging and analysis system for assessing torso deformities , 2008, Comput. Biol. Medicine.

[25]  C. C. Martin,et al.  A real-time ergonomic monitoring system using the Microsoft Kinect , 2012, 2012 IEEE Systems and Information Engineering Design Symposium.

[26]  Jorge Angeles,et al.  Pose-and-twist estimation of a rigid body using accelerometers , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[27]  Nelson G. Durdle,et al.  A support vectors classifier approach to predicting the risk of progression of adolescent idiopathic scoliosis , 2005, IEEE Transactions on Information Technology in Biomedicine.

[28]  Jean Dansereau,et al.  Indices of torso asymmetry related to spinal deformity in scoliosis. , 2002, Clinical biomechanics.

[29]  Tadeusz J. Janik,et al.  Upright static pelvic posture as rotations and translations in 3-dimensional from three 2-dimensional digital images: validation of a computerized analysis. , 2008, Journal of manipulative and physiological therapeutics.

[30]  Sergio Escalera,et al.  Featureweighting in dynamic timewarping for gesture recognition in depth data , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[31]  E. P. Maldonado,et al.  Quantitative assessment of postural alignment in young adults based on photographs of anterior, posterior, and lateral views. , 2011, Journal of manipulative and physiological therapeutics.

[32]  H Labelle,et al.  Estimation of Spinal Deformity in Scoliosis From Torso Surface Cross Sections , 2001, Spine.

[33]  Thomas Horstmann,et al.  Reliability and validity of 4D rasterstereography under dynamic conditions , 2011, Comput. Biol. Medicine.

[34]  Linda Denehy,et al.  Validity of the Microsoft Kinect for assessment of postural control. , 2012, Gait & posture.

[35]  Adrian Hilton,et al.  The Multiple-Camera 3-D Production Studio , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[36]  C H Rivard,et al.  A noninvasive anthropometric technique for measuring kyphosis and lordosis: an application for idiopathic scoliosis. , 2000, Spine.