Signal processing in movement analysis (a state-space approach)

Human Movement Science

Volumn 15, Issue 3, 1996, Pages 389-410

  Fioretti, Sandro ^a  

^a UNIVERSITÀ POLITECNICA DELLE MARCHE   (Italy)

Author keywords

Digital signal processing; Linear Kalman filtering; Non linear Kalman filtering; Numerical differentiation; State space approach

Indexed keywords

EID: 0030167165     PISSN: 01679457     EISSN: None     Source Type: Journal    
DOI: 10.1016/0167-9457(96)00007-3     Document Type: Article

References (48)

1. Anderson, B.D. and J.B. Moore, 1979. Optimal Filtering. Englewood Cliffs: Prentice-Hall.
2. Anderssen, R.S. and P. Bloomfield, 1974. Numerical differentiation procedures for non exact data. Numerical Mathematics 22, 157-182.
3. Andrews, B.J. and D. Jones, 1976. A note on the differentiation of human kinematic data. Proceedings of the 11th International Conference on Medical and Biological Engineering, Ottawa, pp. 88-89.
4. Bellanger, P.R., 1974. Estimation of noise covariances matrices for a linear time-varying stochastic signal. Automatica 10, 267-275.
5. Cappozzo, A., T. Leo and A. Pedotti, 1975. A general computing method for the analysis of human locomotion. Journal of Biomechanics 8, 307-320.
6. Carlsson, B., 1989. Digital differentiating filters and model based fault detection. ISSN 0346-6485, Phd. Thesis, Dept. Technology, Uppsala University, Sweden.
7. Carlsson, B., A. Ahlen and M. Sternad. 1991. Optimal differentiation based on stochastic signal models. IEEE Transactions on ASSP. 40, 341-353.
8. Cheze, L. and S. Fioretti, 1995. Effects of pre-processing techniques on the kinematic descriptors of lower limb joint motion. Proceedings of the XIII International Symposium on Biotelemetry, Williamsburg; Virginia, USA, March 26-30, in press.
9. Cheze, L., B.J. Fregly and J. Dimnet, 1995. A solidification procedure to facilitate kinematic analyses based on video system data. Journal of Biomechanics 28, 879-884.
10. Corradini, M.L., S. Fioretti and T. Leo, 1993. Numerical differentiation in movement analysis: how to standardise the evaluation techniques. Medical and Biological Engineering and Computing 31, 187-197.
11. Craven, P. and G. Wahba, 1979. Smoothing noisy data with spline functions: estimating the correct degree of smoothing by the method of generalized cross-validation. Numerical Mathematics 31, 377-403.
12. Cullum, J., 1971. Numerical differentiation and regularisation. SIAM Journal of Numerical Analysis 8, 254-265.
13. D'Amico, M. and G. Ferrigno, 1990. Technique for the evaluation of derivatives from noisy biomechanical displacement data by a model-based bandwith-selection procedure. Medical and Biological Engineering and Computing 28, 407-415.
14. D'Amico, M. and G. Ferrigno, 1992. Comparison between the more recent techniques for smoothing and derivative assessment in biomechanics. Medical and Biological Engineering and Computing 30, 193-204.
15. Dohrmann, C.R. and H.R. Busby, 1990. A dynamic programming approach to smoothing and differentiation of noisy data with spline functions. In: N. Berme and A. Cappozzo (eds.), Biomechanics of Human Movement. Worthington: Bertec.
16. Dohrmann, C.R., D.M. Trujillo and H.R. Busby, 1988. Smoothing noisy data using dynamic programming and generalized cross-validation. ASME Journal of Biomechanical Engineering 110, 34-47.
17. Fioretti, S. and L. Jetto, 1989. Accurate derivative estimation from noisy data: a state-space approach. International Journal of Systems Science 20, 33-53.
18. Fioretti, S. and L. Jetto, 1991. Filtering of noisy data subject to non-linear constraints. Digital Signal Processing - 91. Proceedings of the International Conference on Digital Signal Processing, September 4-6, Florence, Italy, pp. 105-109.
19. Fioretti, S. and L. Jetto, 1992. A new algorithm for the sequential estimation of the regularization parameter in the spline smoothing problem. Proceedings of the 31st Control Decision Conference, Dec. 16-18, Tucson, Vol. 2, pp. 2245-2246.
20. Fioretti, S. and L. Jetto, 1994a. Low a-priori statistical information model for optimal smoothing and differentiation of noisy signals. International Journal of Adaptive Control and Signal Processing 8, 305-320.
21. Fioretti, S. and L. Jetto. 1994b. Optimal estimation and differentiation of noisy signals with sharp discontinuities. Proc. of the 2nd IEEE Mediterranean Symposium on New Directions in Control and Automation, Chania, Crete, 19-22 June, pp. 380-385.
22. Fioretti, S., L. Jetto and T. Leo, 1990. Reliable in-vivo estimation of the instantaneous helical axis in human segmental movements. IEEE Transactions on Biomedical Engineering, 37, 398-409.
23. Gelb, A., 1974. Applied Optimal Estimation. Cambridge: MIT Press.
24. Golub, G., M. Heath and G. Wahba, 1979. Generalized-cross validation as a method for choosing a good ridge parameter. Technometrics 21, 215-223.
25. Gustafsson, L. and Lanshammar, H., 1977. ENOCH An integrated system for measurement and analysis of human gait. UPTEC 77 23 R, PhD Thesis, Institute of Technology, Uppsala University, Sweden.
26. Hatze, H., 1981. The use of optimally regularized Fourier series for estimating higher-order derivatives of noisy biomechanical data. Journal of Biomechanics 14, 13-18.
27. Igleart, S.C. and C. Leondes, 1974. Estimation of a dispersion parameter in discrete Kalman filtering. IEEE Transaction on Automatic Control AC-19, 262-263.
28. Jazwinski, A.H., 1970. Stochastic Processes and Filtering Theory. New York: Academic Press.
29. Kalman, R.E., 1960. A new approach to linear filtering and prediction problems. Trans ASME Ser. D: Journal of Basic Engineering 82, 35-45.
30. Lanshammar, H., 1982. On precision limits for derivatives numerically calculated from noisy data. Journal of Biomechanics 15, 459-470.
31. Lee, T., 1980. A direct approach to identify the noise covariances of Kalman filtering. IEEE Transaction on Automatic Control AC-25, 841-847.
32. Lesh, M.D., J.M. Mansour and S.P. Simon, 1979. A gait analysis subsystem for smoothing and differentiation of human motion data. ASME Journal of Biomechanical Engineering 101, 205-212.
33. Magnani, G., C. Angeloni, A. Leardini and A. Cappello, 1993. Optimal estimation of rigid body position and attitude from noisy marker coordinates. Proceedings of the XIV Congress of ISB, Paris, France, 4-8 July, pp. 820-821.
34. Mehra, R.K., 1970. On the identification of variances and adaptive Kalman filtering. IEEE Transactions on Automatic Control AC-15, 175-184.
35. Morozov, V.A., 1984. Methods for solving incorrectly posed problems. New York: Springer-Verlag.
36. Myers, K.A., and B.D. Tapley, 1976. Adaptive sequential estimation with unknown noise statistics. IEEE Transaction on Automatoc Control AC-21, 520-523.
37. Pezzack, J.C., R.W. Norman and D.A. Winter, 1977. An assessment of derivative determining techniques used for motion analysis. Journal of Biomechanics 10, 377-382.
38. Slepian, D., 1976. On bandwith. Proceedings of the IEEE 64, 292-300.
39. Soudan, K. and Dierckx, P., 1979. Calculation of derivatives and Fourier coefficients of human motion data, while using spline functions. Journal of Biomechanics 12, 21-26.
40. Tsang, W.L., Glover, J.D. and R.E. Bach, 1981. Identifiability of unknown noise covariances matrices for some special cases of a linear time-invariant, discrete-time dynamic system. IEEE Transactions on Automatic Control AC-26, 970-974.
41. Vaughan, C.L., 1982. Smoothing and differentiation of displacement-time data: an application of splines and digital filtering. International Journal of Bio-Medical Computing 13, 375-386.
42. Winter, D.A., 1990. Biomechanics and Motor Control of Human Movement. John Wiley and Sons, New York.
43. Winter, D.A., Sidwall, H.G. and Hobson, D.A., 1974. Measurement and reduction of noise in kinematics of locomotion. Journal of Biomechanics 7, 157-159.
44. Woltring, H.J., 1985. On optimal smoothing and derivative estimation from noisy displacement data in biomechanics. Human Movement Science 4, 229-245.
45. Woltring, H.J., 1986. A Fortran package for generalised cross-validatory spline smoothing and differentiation. Advances in Engineering Software 8, 104-113.
46. Wood, G.A., 1982. Data smoothing and differentiation procedures in biomechanics. Exercise and Sport Sciences Reviews 10, 308-362.
47. Wood, G.A. and L.S. Jennings, 1979. On the use of spline functions for data smoothing. Journal of Biomechanics 12, 477-479.
48. Zernicke, R.F., G. Caldwell and E.M. Roberts, 1976. Fitting biomechanical data with cubic spline functions. Research Quarterly 47, 9-19.