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Quantitative assessment of the accuracy for three interpolation techniques in kinematic analysis of human movement

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  • Samuel J. Howarth
  • Jack P. Callaghan

Abstract

Marker obstruction during human movement analyses requires interpolation to reconstruct missing kinematic data. This investigation quantifies errors associated with three interpolation techniques and varying interpolated durations. Right ulnar styloid kinematics from 13 participants performing manual wheelchair ramp ascent were reconstructed using linear, cubic spline and local coordinate system (LCS) interpolation from 11–90% of one propulsive cycle. Elbow angles (flexion/extension and pronation/supination) were calculated using real and reconstructed kinematics. Reconstructed kinematics produced maximum elbow flexion/extension errors of 37.1 (linear), 23.4 (spline) and 9.3 (LCS) degrees. Reconstruction errors are unavoidable [minimum errors of 6.7 mm (LCS); 0.29 mm (spline); 0.42 mm (linear)], emphasising careful motion capture system setup must be performed to minimise data interpolation. For the observed movement, LCS-based interpolation (average error of 14.3 mm; correlation of 0.976 for elbow flexion/extension) was most suitable for reconstructing durations longer than 200 ms. Spline interpolation was superior for shorter durations.

Suggested Citation

  • Samuel J. Howarth & Jack P. Callaghan, 2010. "Quantitative assessment of the accuracy for three interpolation techniques in kinematic analysis of human movement," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 13(6), pages 847-855.
    • Handle: RePEc:taf:gcmbxx:v:13:y:2010:i:6:p:847-855
    DOI: 10.1080/10255841003664701
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    Cited by:

    1. Mickaël Tits & Joëlle Tilmanne & Thierry Dutoit, 2018. "Robust and automatic motion-capture data recovery using soft skeleton constraints and model averaging," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-21, July.
    2. Peter Andreas Federolf, 2013. "A Novel Approach to Solve the “Missing Marker Problem” in Marker-Based Motion Analysis That Exploits the Segment Coordination Patterns in Multi-Limb Motion Data," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-13, October.
    3. Øyvind Gløersen & Peter Federolf, 2016. "Predicting Missing Marker Trajectories in Human Motion Data Using Marker Intercorrelations," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-14, March.

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