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Physics-Based Models for Human Gait Analysis

  • Petrissa Zell
  • Bastian Wandt
  • Bodo Rosenhahn
Reference work entry

Abstract

This chapter deals with fundamental methods as well as current research on physics-based human gait analysis. We present valuable concepts that allow efficient modeling of the kinematics and the dynamics of the human body. The resulting physical model can be included in an optimization-based framework. In this context, we show how forward dynamics optimization can be used to determine the producing forces of gait patterns.

To present a current subject of research, we provide a description of a 2D physics-based statistical model for human gait analysis that exploits parameter learning to estimate unobservable joint torques and external forces directly from motion input. The robustness of this algorithm with respect to occluded joint trajectories is shown in a short experiment. Furthermore, we present a method that uses the former techniques for video-based gait analysis by combining them with a nonrigid structure from motion approach. To examine the applicability of this method, a brief evaluation of the performance regarding joint torque and ground reaction force estimation is provided.

Keywords

Computer vision Human motion analysis Physics-based simulation Forward dynamics optimization Data-driven regression 3D motion reconstruction Video-based force estimation 

References

  1. Akhter I, Black MJ (2015) Pose-conditioned joint angle limits for 3D human pose reconstruction. In: IEEE Conference on computer vision and pattern recognition (CVPR 2015). IEEE, pp 1446–1455Google Scholar
  2. Al-Naser M, Söderström U (2012) Reconstruction of occluded facial images using asymmetrical principal component analysis. Integrated Comput Aided Eng 19(3):273–283Google Scholar
  3. Bhat KS, Seitz SM, Popović J, Khosla PK (2002) Computer vision – ECCV 2002: 7th European conference on computer vision copenhagen, Denmark, 2002. In: Proceedings, Part I, chapter computing the physical parameters of rigid-body motion from video, Springer, Berlin/Heidelberg, pp 551–565, 28–31 May 2002Google Scholar
  4. Blajer W, Dziewiecki K, Mazur Z (2007) Multibody modeling of human body for the inverse dynamics analysis of sagittal plane movements. Multibody Sys Dyn 18(2):217–232CrossRefzbMATHGoogle Scholar
  5. Bregler C, Hertzmann A, Biermann H (2000) Recovering non-rigid 3D shape from image streams. In: IEEE Conference on computer vision and pattern recognition (CVPR). IEEE, pp 690–696Google Scholar
  6. Brubaker MA, Fleet DJ (2008) The kneed walker for human pose tracking. In: IEEE conference on, computer vision and pattern recognition, 2008 (CVPR 2008). pp 1–8, June 2008Google Scholar
  7. Brubaker MA, Sigal L, Fleet DJ (2009) Estimating contact dynamics. In: IEEE 12th international conference on Computer vision 2009. IEEE, pp 2389–2396Google Scholar
  8. Dai Y, Li H (2012) A simple prior-free method for non-rigid structure-from-motion factorization. In: Conference on computer vision and pattern recognition (CVPR), CVPR’12, IEEE Computer Society, Washington DC, pp 2018–2025, 2012Google Scholar
  9. Fang AC, Pollard NS (2003) Efficient synthesis of physically valid human motion. ACM Trans Graph 22(3):417–426CrossRefGoogle Scholar
  10. Fregly BJ, Reinbolt JA, Rooney KL, Mitchell KH, Chmielewski TL (2007) Design of patient-specific gait modifications for knee osteoarthritis rehabilitation. IEEE Trans Biomed Eng 54(9):1687–1695CrossRefGoogle Scholar
  11. Hamsici O, Gotardo P, Martinez A (2011) Learning spatially-smooth mappings in non-rigid structure from motion. In: European conference on computer vision (ECCV). Springer, Berlin/HeidelbergGoogle Scholar
  12. Johnson L, Ballard DH (2014) Efficient codes for inverse dynamics during walking. In: Proceedings of the twenty-eighth AAAI press conference on artificial intelligence, AAAI’14. AAAI Press, pp 343–349Google Scholar
  13. Kazemi V, Burenius M, Azizpour H, Sullivan J (2013) Multi-view body part recognition with random forests. In: British machine vision conference (BMVC). BMVC Press, BristolGoogle Scholar
  14. Liu CK, Hertzmann A, Popović Z (2005) Learning physics-based motion style with nonlinear inverse optimization. ACM Trans Graph 24(3):1071–1081CrossRefGoogle Scholar
  15. Mayers D, Sli E (2003) An introduction to numerical analysis. Cambridge University Press, CambridgeGoogle Scholar
  16. Park HS, Shiratori T, Matthews I, Sheikh Y (2010) 3D reconstruction of a moving point from a series of 2D projections. In: European conference on computer vision (ECCV). Springer, Berlin/HeidelbergGoogle Scholar
  17. Powell MJD (1978) Numerical analysis. In: Proceedings of the Biennial Conference held at Dundee, chapter A fast algorithm for nonlinearly constrained optimization calculations, Springer, Berlin/Heidelberg, pp 144–157, June 28–July 1 1977Google Scholar
  18. Powers CM (2010) The influence of abnormal hip mechanics on knee injury: a biomechanical perspective. J Orthop Sports Phys Ther 40(2):42–51CrossRefGoogle Scholar
  19. Ramakrishna V, Kanade T, Sheikh YA (2012) Reconstructing 3D human pose from 2D image landmarks. In: European conference on computer vision (ECCV). Springer, Berlin/HeidelbergGoogle Scholar
  20. Safonova A, Hodgins JK, Pollard NS (2004) Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces. ACM Trans Graph 23(3):514–521CrossRefGoogle Scholar
  21. Schmalz T, Blumentritt S, Jarasch R (2002) Energy expenditure and biomechanical characteristics of lower limb amputee gait: the influence of prosthetic alignment and different prosthetic components. Gait Posture 16(3):255–263CrossRefGoogle Scholar
  22. Schwab AL, Delhaes GMJ (2009) Lecture notes multibody dynamics B, wb1413Google Scholar
  23. Sok KW, Kim M, Lee J (2007) Simulating biped behaviors from human motion data. ACM Trans Graph 26(3):107:1–107:9CrossRefGoogle Scholar
  24. Spong M, Hutchinson S, Vidyasagar M (2005) Robot modeling and control. WileyGoogle Scholar
  25. Steinparz F (1985) Co-ordinate transformation and robot control with denavit-hartenberg matrices. J Microcomput Appl 8(4):303–316CrossRefGoogle Scholar
  26. Stelzer M, von Stryk O (2006) Efficient forward dynamics simulation and optimization of human body dynamics. ZAMM – J Appl Math Mech/Zeitschrift fr Angewandte Mathematik und Mechanik 86(10):828–840MathSciNetCrossRefzbMATHGoogle Scholar
  27. Tomasi C, Kanade T (1992) Shape and motion from image streams under orthography: a factorization method. Int J Comput Vis 9:137–154CrossRefGoogle Scholar
  28. Torresani L, Hertzmann A, Bregler C (2003) Learning non-rigid 3D shape from 2D motion. In: Thrun S, Saul LK, Schölkopf B (eds) Neural information processing systems (NIPS). MIT Press, Cambridge, MAGoogle Scholar
  29. Torresani L, Hertzmann A, Bregler C (2008) Nonrigid structure-from-motion: estimating shape and motion with hierarchical priors. In: IEEE Transactions pattern analysis and machine intelligence, IEEE, 21 March 2008Google Scholar
  30. Troje NF (2002a) Decomposing biological motion: a framework for analysis and synthesis of human gait patterns. J Vis 2(5):371–387CrossRefGoogle Scholar
  31. Troje NF (2002b) The little difference: Fourier based synthesis of gender-specific biological motion. AKA Press, Berlin, pp 115–120Google Scholar
  32. Vondrak M, Sigal L, Jenkins OC (2008) Physical simulation for probabilistic motion tracking. In: IEEE conference on computer vision and pattern recognition, 2008 (CVPR 2008), pp 1–8, June 2008. IEEEGoogle Scholar
  33. Wandt B, Ackermann H, Rosenhahn B (2015) 3d human motion capture from monocular image sequences. In: IEEE conference on computer vision and pattern recognition workshops, June 2015. IEEEGoogle Scholar
  34. Wandt B, Ackermann H, Rosenhahn B (2016) 3d reconstruction of human motion from monocular image sequences. IEEE Trans Pattern Anal Mach Intell 38(8):1505–1516CrossRefGoogle Scholar
  35. Wang C, Wang Y, Lin Z, Yuille A, Gao W (2014) Robust estimation of 3d human poses from a single image. In: IEEE Conference on computer vision and pattern recognition (CVPR). IEEEGoogle Scholar
  36. Wei X, Min J, Chai J (2011) Physically valid statistical models for human motion generation. ACM Trans Graph 30(3):19:1–19:10CrossRefGoogle Scholar
  37. Wren CR, Pentland AP (1998) Dynamic models of human motion. In: Proceedings of the third IEEE internatonal conference on automatic face and gesture recognition, Nara, April 1998.Google Scholar
  38. Xiang Y, Chung H-J, Kim JH, Bhatt R, Rahmatalla S, Yang J, Marler T, Arora JS, Abdel-Malek K (2010) Predictive dynamics: an optimization-based novel approach for human motion simulation. Struct Multidiscip Optim 41(3):465–479MathSciNetCrossRefzbMATHGoogle Scholar
  39. Zell P, Rosenhahn B (2015) Pattern recognition: 37th German conference, GCPR 2015. In: Proceedings, chapter A physics-based statistical model for human gait analysis, Springer International Publishing, Aachen, Germany, October 7–10, 2015, pp 169–180.Google Scholar
  40. Zordan VB, Majkowska A, Chiu B, Fast M (2005) Dynamic response for motion capture animation. ACM Trans Graph 24(3):697–701CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institut für InformationsverarbeitungLeibniz Universität HannoverHannoverGermany

Section editors and affiliations

  • William Scott Selbie
    • 1
  1. 1.Has-Motion Inc.KingstonCanada

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