Digital Filtering of Three-Dimensional Lower Extremity Kinematics: an Assessment

Open access


Errors in kinematic data are referred to as noise and are an undesirable portion of any waveform. Noise is typically removed using a low-pass filter which removes the high frequency components of the signal. The selection of an optimal frequency cut-off is very important when processing kinematic information and a number of techniques exists for the determination of an optimal frequency cut-off. Despite the importance of cut-off frequency to the efficacy of kinematic analyses there is currently a paucity of research examining the influence of different cut-off frequencies on the resultant 3-D kinematic waveforms and discrete parameters. Twenty participants ran at 4.0 m•s-1 as lower extremity kinematics in the sagittal, coronal and transverse planes were measured using an eight camera motion analysis system. The data were filtered at a range of cut-off frequencies and the discrete kinematic parameters were examined using repeated measures ANOVA’s. The similarity between the raw and filtered waveforms were examined using intra-class correlations. The results show that the cut-off frequency has a significant influence on the discrete kinematic measure across displacement and derivative information in all three planes of rotation. Furthermore, it was also revealed that as the cut-off frequency decreased the attenuation of the kinematic waveforms became more pronounced, particularly in the coronal and transverse planes at the second derivative. In conclusion, this investigation provides new information regarding the influence of digital filtering on lower extremity kinematics and re-emphasizes the importance of selecting the correct cut-off frequency.

Anderssen RS, Bloomfield P. Numerical differentiation procedures for non-exact data. Numer Math, 1974; 22: 1157-1182

Bartlett R. Introduction to Sports Biomechanics: Analyzing Human Movement Patterns 2nd Ed. London, UK: Routledge; 2007

DAmico M, Ferrigno G. Technique for the evaluation of derivatives from noisy biomechamcal displacement data using a model-based-bandwidth-selection procedure. Med Biol Eng Comput, 1990; 28: 407-415

Enoka RM. Neuromechanics of Human Movement. 4th Ed. Champaign, IL: Human Kinetics; 2008

Harris FJ. On the use of window functions for harmonic analysis with discrete Fourier transform. Proc. IEEE, 1978; 66: 51-83

Gautam JK, Kumar A RS. Windows: A tool in signal processing. IETE Tech. Rev, 1995; 12: 217- 226

Griffiths IW. Principles of Biomechanics & Motion Analysis. Baltimore, MD: Lippincott Williams & Wilkins; 2006

Hatze H. The use of optimally regularised Fourier series for estimating higher-order derivatives of noisy biomechanical data. J Biomech, 1981; 14: 13-18

Mills C, Joanna S, Wood L. A protocol for monitoring soft tissue motion under compression garments during drop landings. J Biomech, 2011; 44: 1821-1823

Jackson K. Fitting mathematical function to biomechanical data. IEEE Trans Biomed Eng, 1979; 26: 172-124

Robertson DG, Caldwell G, Hamill J, Kamen G, Whitlesey SN. Research Methods in Biomechanics. Champaign, IL: Human Kinetics; 2004

Winter DA. Biomechanics and Motor Control of Human Movement. 3rd Ed. Hoboken, NJ: Wiley; 2005

McLaughlin TM, Diltman CJ, Lardner TJ. Biomechanical analysis with cubic spline functions. Res Q, 1977; 48: 569-582

Pezzack JC, Norman RW, Winter DA. Assessment of derivative determining techniques used for motion analysis. J Biomech, 1977; 10: 377-382

Robertson DGE, Dowling JJ. Design and responses of Butterworth and critically damped digital filters. JElectromyogr Kines, 2003; 13: 569-573

Sinclair J, Hobbs SJ, Edmundson CJ, Brooks D. Evaluation of kinematic methods of identifying foot strike and toe-off during running. Int J S Sci Eng, 2011; 5: 188-192 Sinclair J, Taylor PJ, Edmundson CJ, Brooks D, Hobbs SJ. The influence of footwear kinetic, kinematic and electromographical parameters on the energy requirements of steady state running. Mov S Sci, 2012; 80: 39-49

Sinclair J, Richards J, Taylor PJ, Edmundson CJ, Brooks D, Hobbs SJ. Three-dimensional kinematics of treadmill and overground running. S Biomech, 2013a; 12: 272-282

Sinclair J, Greenhalgh A, Edmundson CJ, Hobbs SJ. The efficacy of barefoot and shod running and shoes designed to mimic barefoot running. Footwear Sci, 2013b; 5: 45-53

Smith G. Padding point extrapolation techniques for the Butterworth digital filter. J Biomech, 1989; 22: 967-971

Soudan K, Dierckx P. Calculation of derivatives and Fourier-coefficients of human motion data, while using spline functions. J Biomech, 1979; 12: 21-26

Stankovic L. On the time-frequency analysis based filtering. Ann Télécommun, 2000; 55: 216-225

Vaughan CL. Smoothing and differentiation of displacement - time data: an application of splines and digital filtering. Int J Bio-Med Comp, 1982; 13: 375-396

Walker JS. Fast Fourier Transforms. 2nd Edition. CRC Press, Boca Raton; 1996

Welch P. The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. IEEE Trans. on Audio and Electroacoustics, 1967; 15: 70

Winter DA, Sidwall HG, Hobson DA. Measurement and reduction of noise in kinematics of locomotion. JBiomech, 1974; 7: 157-159

Winter DA. Biomechanics and motor control of human movement. John Wiley and Sons, Inc., New York; 1990

Wood GA, Jennings LS. On the use of spline functions for data smoothing. J Biomech, 1979; 12: 477-479

Yu B. Determination of the optimum cut-off frequency in the digital filter data smoothing procedure. Proceedings of the 12th International Congress of Biomechanics, University of California. Los Angeles; 1989

Yu B, Gabriel D, Noble L, An KN. Estimate of the optimal cutoff frequency for the butterworth low-pass digital filter. J App Biomech, 1999; 15: 318-325

Zernicke RF, Caldwell G, Roberts EM. Fitting biomechanical data with cubic spline functions. Res. Q, 1976; 47: 9-19

Journal of Human Kinetics

The Journal of Academy of Physical Education in Katowice

Journal Information

IMPACT FACTOR 2017: 1.174
5-year IMPACT FACTOR: 1.634

CiteScore 2017: 1.31

SCImago Journal Rank (SJR) 2017: 0.516
Source Normalized Impact per Paper (SNIP) 2017: 0.906

Cited By


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 178 175 14
PDF Downloads 59 58 7