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  4. Learning an infant body model from RGB-D data for accurate full body motion analysis
 
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2018
Conference Paper
Title

Learning an infant body model from RGB-D data for accurate full body motion analysis

Abstract
Infant motion analysis enables early detection of neurodevelopmental disorders like cerebral palsy (CP). Diagnosis, however, is challenging, requiring expert human judgement. An automated solution would be beneficial but requires the accurate capture of 3D full-body movements. To that end, we develop a non-intrusive, low-cost, lightweight acquisition system that captures the shape and motion of infants. Going beyond work on modeling adult body shape, we learn a 3D Skinned Multi-Infant Linear body model (SMIL) from noisy, low-quality, and incomplete RGB-D data. SMIL is publicly available for research purposes at http://s.fhg.de/smil. We demonstrate the capture of shape and motion with 37 infants in a clinical environment. Quantitative experiments show that SMIL faithfully represents the data and properly factorizes the shape and pose of the infants. With a case study based on general movement assessment (GMA), we demonstrate that SMIL captures enough information to allow medical assessment. SMIL provides a new tool and a step towards a fully automatic system for GMA.
Author(s)
Hesse, Nikolas  
Pujades, Sergi
Romero, Javier
Black, Michael J.
Bodensteiner, Christoph  
Arens, Michael  
Hofmann, Ulrich G.
Tacke, Uta
Hadders-Algra, Mijna
Weinerger, Raphael
Müller-Felber, Wolfgang
Schröder, Sebastian A.
Mainwork
Medical Image Computing and Computer Assisted Intervention, MICCAI 2018  
Conference
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2018  
Open Access
DOI
10.1007/978-3-030-00928-1_89
Additional full text version
Landing Page
Language
English
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Keyword(s)
  • body model

  • data-driven

  • cerebral palsy

  • motion analysis

  • pose tracking

  • general movement assessment

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