Advertisement

Markerless Motion Analysis for Early Detection of Infantile Movement Disorders

  • Nikolas Hesse
  • A. Sebastian Schroeder
  • Wolfgang Müller-Felber
  • Christoph Bodensteiner
  • Michael Arens
  • Ulrich G. Hofmann
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 65)

Abstract

The analysis of spontaneous movements provides valuable information for diagnosing infantile movement disorders. However, analysis is time-consuming and interpretation requires well-trained experts. We present an automated system that captures 3D joint positions and head rotation of infants without attached markers or sensors. We introduce motion parameters of head, trunk, upper and lower limbs of both body sides that are related to range, variability, and symmetry of motions and offer objective diagnostic information for assessment of motor behavior. We analyze 6 recordings of 5 infants who are at high-risk of impaired motor development, and show how the system highlights movement characteristics that hint at disorders.

Keywords

Motion analysis infants diagnostics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Valla Lisbeth, Wentzel-Larsen Tore, Hofoss Dag, Slinning Kari. Prevalence of suspected developmental delays in early infancy: results from a regional population-based longitudinal study BMC pediatrics. 2015;15:215Google Scholar
  2. 2.
    Spittle Alicia, Orton Jane, Anderson Peter J, Boyd Roslyn, Doyle Lex W. Early developmental intervention programmes provided post hospital discharge to prevent motor and cognitive impairment in preterm infants The Cochrane Library. 2015Google Scholar
  3. 3.
    Hesse Nikolas, Stachowiak Gregor, Breuer Timo, Arens Michael. Estimating Body Pose of Infants in Depth Images Using Random Ferns in IEEE International Conference on Computer Vision Workshops:35–43 2015Google Scholar
  4. 4.
    Meinecke L, Breitbach-Faller N, Bartz C, Damen R, Rau G, Disselhorst-Klug C. Movement analysis in the early detection of newborns at risk for developing spasticity due to infantile cerebral palsy Human movement science. 2006;25:125–144Google Scholar
  5. 5.
    Karch Dominik, Kim Keun-Sun, Wochner Katarzyna, Pietz Joachim, Dickhaus Hartmut, Philippi Heike. Quantification of the segmental kinematics of spontaneous infant movements Journal of biomechanics. 2008;41:2860–2867Google Scholar
  6. 6.
    Stahl Annette, Schellewald Christian, Stavdahl Øyvind, Aamo Ole Morten, Adde Lars, Kirkerød Harald. An optical flow-based method to predict infantile cerebral palsy IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2012;20:605–614Google Scholar
  7. 7.
    Chen Lulu, Wei Hong, Ferryman James. A survey of human motion analysis using depth imagery Pattern Recognition Letters. 2013;34:1995–2006Google Scholar
  8. 8.
    Olsen Mikkel Damgaard, Herskind Anna, Nielsen Jens Bo, Paulsen Rasmus Reinhold. Model-Based Motion Tracking of Infants in European Conference on Computer Vision Workshops:673–685 2014Google Scholar
  9. 9.
    Serrano Miguel M, Chen Yu-Ping, Howard Ayanna, Vela Patricio A. Lower limb pose estimation for monitoring the kicking patterns of infants in IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC):2157–2160 2016Google Scholar
  10. 10.
    Marcroft Claire, Khan Aftab, Embleton Nicholas D, Trenell Michael, Plötz Thomas. Movement recognition technology as a method of assessing spontaneous general movements in high risk infants Frontiers in neurology. 2014;5Google Scholar
  11. 11.
    Özuysal Mustafa, Fua Pascal, Lepetit Vincent. Fast keypoint recognition in ten lines of code in IEEE Conference on Computer Vision and Pattern Recognition:1–8 2007Google Scholar
  12. 12.
    King Davis E.. Dlib-ml: A Machine Learning Toolkit Journal of Machine Learning Research. 2009;10:1755-1758Google Scholar
  13. 13.
    King Davis E. Max-margin object detection arXiv preprint arXiv:1502.00046. 2015
  14. 14.
    Kazemi Vahid, Sullivan Josephine. One millisecond face alignment with an ensemble of regression trees in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition:1867–1874 2014Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Nikolas Hesse
    • 1
  • A. Sebastian Schroeder
    • 2
  • Wolfgang Müller-Felber
    • 2
  • Christoph Bodensteiner
    • 1
  • Michael Arens
    • 1
  • Ulrich G. Hofmann
    • 3
  1. 1.Fraunhofer Institute of Optronics System Technologies and Image Exploitation IOSBEttlingenGermany
  2. 2.Department of Paediatric Neurology and Developmental MedicineDr. von Hauner Children’s Hospital Ludwig-Maximilians-Universität (LMU)MunichGermany
  3. 3.Section for Neuroelectronic Systems Neurosurgery Medical Center University of Freiburg Germany Faculty of Medicine University of Freiburg Germany Freiburg Institute for Advanced Studies (FRIAS)University of FreiburgFreiburgGermany

Personalised recommendations