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Inference on the Joint Center of Rotation by Covariance Pattern Models

  • Kim, Jinuk (Department of Physical Education, Kunsan National University)
  • Received : 2018.03.07
  • Accepted : 2018.06.04
  • Published : 2018.06.30

Abstract

Objective: In a statistical linear model estimating the center of rotation of a human hip joint, which is the parameter related to the mean of response vectors, assumptions of homoscedasticity and independence of position vectors measured repeatedly over time in the model result in an inefficient parameter. We, therefore, should take into account the variance-covariance structure of longitudinal responses. The purpose of this study was to estimate the efficient center of rotation vector of the hip joint by using covariance pattern models. Method: The covariance pattern models are used to model various kinds of covariance matrices of error vectors to take into account longitudinal data. The data acquired from functional motions to estimate hip joint center were applied to the models. Results: The results showed that the data were better fitted using various covariance pattern models than the general linear model assuming homoscedasticity and independence. Conclusion: The estimated joint centers of the covariance pattern models showed slight differences from those of the general linear model. The estimated standard errors of the joint center for covariance pattern models showed a large difference with those of the general linear model.

Keywords

References

  1. Cereatti, A., Croce, U. D. & Cappozzo, A. (2006). Reconstruction of skeletal movement using skin markers: Comparative assessment of bone pose estimators. Journal of Neuroengineering and Rehabilitation, 3: 7, 1-12. https://doi.org/10.1186/1743-0003-3-1
  2. Cnaan, A., Laird, N. M. & Slasor, P. (1997). Tutorial in biostatistics: Using the general linear mixed model to analyse unbalanced repeated measures and longitudinal data. Statistics in Medicine, 16, 2349-2380. https://doi.org/10.1002/(SICI)1097-0258(19971030)16:20<2349::AID-SIM667>3.0.CO;2-E
  3. Diggle, P. J., Heagerty, P., Liang, K. Y. & Zeger, S. L. (2002). Analysis of longitudinal data (2nd ed.). New York, NY: Oxford University Press.
  4. De Rosario, H., Page, A., Besa, A. & Valera, A. (2013). Propagation of soft tissue artifacts to the center of rotation: A model for the correction of functional calibration techniques. Journal of Biomechanics, 46, 2619-2625. https://doi.org/10.1016/j.jbiomech.2013.08.006
  5. Ehrig, R. M., Taylor, W. R., Duda, G. N. & Heller, M. O. (2007). A survey of formal methods for determining the centre of rotation of ball joint. Journal of Biomechanics, 40, 2150-2157. https://doi.org/10.1016/j.jbiomech.2006.10.026
  6. Fitzmaurice, G. M., Laird, N. M. & Ware, J. H. (2004). Applied longitudinal analysis. Hoboken, NJ: John Wiley & Sons.
  7. Galecki, A. & Burzykowski, T. (2013). Linear mixed-effects models using R: A step-by-step approach. New York, NY: Springer.
  8. Hedeker, D. & Gibbons, R. D. (2006). Longitudinal data analysis. Hoboken, NJ: John Wiley & Sons.
  9. Jennrich, R. I. & Schluchter, M. D. (1986). Unbalanced repeated-measures models with structured covariance matrices. Biometrics, 42, 805-820. https://doi.org/10.2307/2530695
  10. Kim, J. (2017). A statistical model for marker position in biomechanics. Korean Journal of Sport Biomechanics, 27, 67-74. https://doi.org/10.5103/KJSB.2017.27.1.67
  11. Kim, J. (2011). Comparison among functional methods of axis of rotation suitable for describing human joint motion. Korean Journal of Sport Biomechanics, 21, 449-458. https://doi.org/10.5103/KJSB.2011.21.4.449
  12. Kim, J. (2013). The comparison of sphere fitting methods for estimating the center of rotation on a human joint. Korean Journal of Sport Biomechanics, 23, 53-62. https://doi.org/10.5103/KJSB.2013.23.1.053
  13. Laird, N. M. & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38, 963-974. https://doi.org/10.2307/2529876
  14. Littell, R. C., Pendergast, J. & Natarajan, R. (2000). Tutorial in biostatistics: Modelling covariance structure in the analysis of repeated measures data. Statistics in Medicine, 19, 1793-1819. https://doi.org/10.1002/1097-0258(20000715)19:13<1793::AID-SIM482>3.0.CO;2-Q
  15. Liu, S., Rovine, M. J. & Molenaar, P. C. M. (2012). Selecting a linear mixed model for longitudinal data: Repeated measures analysis of variance, covariance pattern model, and growth curve approaches. Psychological Methods, 17, 15-30. https://doi.org/10.1037/a0026971
  16. Ojeda, J., Martinez-Reina, J. & Mayo, J. (2014). A method to evaluate human skeletal models using marker residuals and global optimization. Machanism and Machine Theory, 73, 259-272. https://doi.org/10.1016/j.mechmachtheory.2013.11.003
  17. Piazza, S. J., Erdemir, A., Okita, N. & Cavanagh, P. R. (2004). Assessment of the functional method of hip joint center location subject to reduced range of hip motion. Journal of Biomechanics, 37, 349-356. https://doi.org/10.1016/S0021-9290(03)00288-4
  18. Pinheiro, J. C. & Bates, D. M. (2000). Mixed-effects models in S and SPLUS. New York, NY: Springer Verlag.
  19. Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., Heisterkamp, S., Van Willigen, B. & Maintainer, R. (2017). Package 'nlme'. Linear and Nonlinear Mixed Effects Models, version, 3-1.
  20. Schluchter, M. D. (1988). Analysis of incomplete multivariate data using linear models with structured covariance matrices. Statistics in Medicine, 7, 317-324. https://doi.org/10.1002/sim.4780070132
  21. Siston, R. A. & Delp, S. L. (2006). Evaluation of a new algorithm to determine the hip joint center. Journal of Biomechanics, 39, 125-130. https://doi.org/10.1016/j.jbiomech.2004.10.032
  22. Ware, J. H. (1985). Linear models for the analysis of longitudinal studies. The American Statistician, 39, 95-101.
  23. Wolfinger, R. (1993). Covariance structure selection in general mixed models. Communications in Statistics-Simulation and Computation, 22, 1079-1106. https://doi.org/10.1080/03610919308813143
  24. Wolfinger, R. (1996). Heterogeneous variance-covariance structures for repeated measures. Journal of Agricultural, Biological, and Environmental Statistics, 1, 205-230.
  25. Wu, G., Siegler, S., Allard, P., Kirtley, C., Leardini, A., Rosenbaum, D., . . . & Stokes, I. (2002). ISB recommendation on definitions of joint coordinate system of various joints for the reporting of human joint motion-part I: ankle, hip, and spine, Journal of Biomechanics, 35, 543-548. https://doi.org/10.1016/S0021-9290(01)00222-6