2D Markerless Gait Analysis

  • Michela Goffredo
  • John N. Carter
  • Mark S. Nixon
Part of the IFMBE Proceedings book series (IFMBE, volume 22)

Abstract

We present a 2D gait analysis system which is completely markerless and extracts kinematic information by analyzing video sequences obtained from an RGB video camera. These properties make the proposed approach particularly suitable in medical contexts where visual gait observation is still a recognised procedure or the invasiveness and high costs of marker-based systems can not be afforded. Markerless motion estimation literature for medical gait analysis is generally 2D oriented, since the majority of joints dysfunctions related to gait occur in the sagittal plane. Most of the approaches are based on time consuming human body models or need human-intervention. Conversely, the method we present this contribution is silhouette-based, completely automatic and uses information on the human body anthropometric proportions for the estimation of the lower limbs’ pose in the sagittal plane with good accuracy and low computational cost. Tests on a large number of synthetic and real video sequences with normal gait have been performed. Different frame rates, image resolutions and noises have been considered. The obtained results, in terms of sagittal joint angles, have been compared with the typical trends found in biomechanical studies. The performance of the proposed method is particularly encouraging for its appliance in the real medical context.

Keywords

Markerless Gait Analysis Image Processing 2D Human Motion Analysis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baker R (2006) Gait analysis methods in rehabilitation. J Neuroengineering Rehabil 3:4CrossRefGoogle Scholar
  2. 2.
    Davis RB, DeLuca PA (1996) Clinical Gait Analysis: Current Methods and Future Directions. In Human Motion Analysis, GF Harris and PA Smith Editors, IEEE Press, Piscataway, New JerseyGoogle Scholar
  3. 3.
    Sutherland DH (2002) The evolution of clinical gait analysis-Part II Kinematics. Gait and Posture, 16:2: 159–179CrossRefMathSciNetGoogle Scholar
  4. 4.
    Davis RI, Ounpuu S, Tyburski D, Gage J (1991) A gait data collection and reduction technique. Hum Mov Sci, 10:575–587CrossRefGoogle Scholar
  5. 5.
    Brady R, Pavol M, Owing T, Grabiner M (2000) Foot displacement but not velocity predicts the outcome of a slip induced in young subjects while walking. J Biomech 33:803–808CrossRefGoogle Scholar
  6. 6.
    The Pathokinesiology Department & The Physical Therapy Department Rancho Los Amigos Medical Center (1996) Observational Gait Analysis HandbookGoogle Scholar
  7. 7.
    Lord SE, Halligan PW, Wade DT (1998) Visual gait analysis: the development of a clinical assessment and scale. Clin Rehabil 12:107–119CrossRefGoogle Scholar
  8. 8.
    Schrotter G (2005) Markerless tracking and surface measurements in biomechanical applications, IASTED Proc., Int. Conf. on Robotics and Applications, Cambridge, USA, 2005Google Scholar
  9. 9.
    Corazza S, Mundermann L, Chaudhari A M, Demattio T, Cobelli C, Andriacchi T P (2006) A markerless motion capture system to study musculoskeletal biomechanics: visual hull and simulated annealing approach. Annals of Biomedical Engineering 34:6: 1019–29CrossRefGoogle Scholar
  10. 10.
    Zhanfeng Y, Liang Z, Chellappa R (2003) View synthesis of articulating humans using visual hull. ICME Proc., vol. 1, Int. Conf. on Multimedia and Expo, Baltimore, USA, 2003, pp 489–92Google Scholar
  11. 11.
    Perry J (1992) Gait Analysis: Normal and Pathological Function. McGraw Hill, New YorkGoogle Scholar
  12. 12.
    Saboune J, Charpillet F (2005) Markerless human motion capture for gait analysis. EMBEC Proc., European Medical and Biological Engineering Conf. Advancement of Medicine and Health Care through Technology, Prague, 2005Google Scholar
  13. 13.
    Orrite-Urunuela C, Del Rincon J, Herrero-Jaraba J, Rogez G (2004) 2D silhouette and 3D skeletal models for human detection and tracking. IEEE Proc., Int. Conf. on Pattern Recognition, vol. 4, pp 244–247, Cambridge, UK, 2004Google Scholar
  14. 14.
    Goffredo M, Schmid M, Conforto S, Carli M, Neri A, D’Alessio T (2005) Coarse-to-fine markerless gait analysis based on PCA and Gauss-Laguerre decomposition. SPIE Proc., Medical Imaging: Image Processing, vol. 5747, pp.1076–1084, San Diego, USA, 2005.Google Scholar
  15. 15.
    Ju S, Black M, Yacoob Y (1996) Cardboard people: a parameterized model of articulated image motion. IEEE Proc., Int. Conf. Automatic Face and Gesture, pp 38–44, Killington, USA, 1996Google Scholar
  16. 16.
    Wren CR, Azarbayejani A, Darrell T, Pentland AP (1997) Pfinder: real-time tracking of the human body. IEEE Trans on Pattern Analysis and Machine Intelligence, 19:7:780–785CrossRefGoogle Scholar
  17. 17.
    Haralick RM, Shapiro LG (1992) Computer and Robot Vision, volume 1. Addison-WesleyGoogle Scholar
  18. 18.
    Dempster WT, Gaughran GRL (1965) Properties of body segments based on size and weight. American Journal of Anatomy 120:33–54CrossRefGoogle Scholar
  19. 19.
    Nixon MS, Aguado A (2007) Feature Extraction & Image Processing. 2nd edition, Academic PressGoogle Scholar
  20. 20.
    Murray MP, Drought AB, Kory RC (1964) Walking patterns of normal men. J Bone Joint Surgery, 46:2:335–360Google Scholar
  21. 21.
    Netravali AN, Haskell BG (1995) Digital Pictures: Representation, Compression, and Standards. Plenum Press, New YorkGoogle Scholar
  22. 22.
    Winter DA (1990) The Biomechanics and Motor Control of Human Movement. John Wiley & SonsGoogle Scholar
  23. 23.
    Murray MP (1967) Gait as a Total Pattern of Movement. Am J Physical Medicine 46:1:290–329Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Michela Goffredo
    • 1
  • John N. Carter
    • 1
  • Mark S. Nixon
    • 1
  1. 1.ECSUniversity of SouthamptonUK

Personalised recommendations