Body Part Tracking of Infants

Motion tracking is a widely used technique to analyze and measure adult human movement. However, these methods cannot be transferred directly to motion tracking of infants due to the big differences in the underlying human model. However, motion tracking of infants can be used for automatic analysis of infant development and might be able to tell something about possible motor disabilities such as cerebral palsy. In this paper, we address marker less 3D body part detection of infants using a widely available depth sensor and discuss some of the major challenges that arise. We present a method to detect and identify a set of the anatomical extremities and the results are evaluated based on manually annotated 3D positions.

[1]  Guozhi Tao,et al.  Quantifying motion in video recordings of neonatal seizures by regularized optical flow methods , 2005, IEEE Transactions on Image Processing.

[2]  Hans-Peter Seidel,et al.  A data-driven approach for real-time full body pose reconstruction from a depth camera , 2011, 2011 International Conference on Computer Vision.

[3]  Sebastian Thrun,et al.  Real-time identification and localization of body parts from depth images , 2010, 2010 IEEE International Conference on Robotics and Automation.

[4]  Xiaojun Wu,et al.  Model based human motion tracking using probability evolutionary algorithm , 2008, Pattern Recognit. Lett..

[5]  Sergio Escalera,et al.  Graph cuts optimization for multi-limb human segmentation in depth maps , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..

[7]  Bruno Raffin,et al.  3D Skeleton-Based Body Pose Recovery , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[8]  Parsa Rahmanpour,et al.  Features for Movement based Prediction of Cerebral Palsy , 2009 .

[9]  G Rau,et al.  Movement analysis in the early detection of newborns at risk for developing spasticity due to infantile cerebral palsy. , 2006, Human movement science.

[10]  I. Novak,et al.  Cerebral palsy--don't delay. , 2011, Developmental disabilities research reviews.

[11]  Giovanni Cioni,et al.  Abstract booklet Publications on Prechtl’s Method on the Qualitative Assessment of General Movements in Preterm, Term and Young Infants , 2014 .

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

[13]  K. Himmelmann,et al.  Epidemiology of cerebral palsy. , 2013, Handbook of clinical neurology.

[14]  O. M. Aamo,et al.  An Optical Flow-Based Method to Predict Infantile Cerebral Palsy , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[15]  Andreas Berg Modellbasert klassifisering av spedbarns bevegelser , 2008 .

[16]  Nassir Navab,et al.  Estimating human 3D pose from Time-of-Flight images based on geodesic distances and optical flow , 2011, Face and Gesture 2011.

[17]  Sarah McIntyre,et al.  A systematic review of risk factors for cerebral palsy in children born at term in developed countries , 2013, Developmental medicine and child neurology.

[18]  Lale Akarun,et al.  Real time hand pose estimation using depth sensors , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[19]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

[20]  Ronald L. Rivest,et al.  Introduction to Algorithms, third edition , 2009 .

[21]  B. Dan,et al.  Proposed definition and classification of cerebral palsy, April 2005. , 2005, Developmental medicine and child neurology.

[22]  Ruigang Yang,et al.  Accurate 3D pose estimation from a single depth image , 2011, 2011 International Conference on Computer Vision.

[23]  Ashutosh Saxena,et al.  Pose Estimation from a Single Depth Image for Arbitrary Kinematic Skeletons , 2011, ArXiv.