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

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.

Single leg squat ratings by clinicians are reliable and predict excessive hip internal rotation moment

“…Data was labelled in Vicon Nexus (version 2.1) and processed in Visual 3D (C-motion version 6.0, Rochelle, USA). Kinematic data was filtered using a 6 Hz low pass bidirectional Butterworth filter [32] and gaps were interpolated using a 3rd order least squares fit (maximum 10 frames) [33]. Kinetic data was filtered separately at 50 Hz [34].…”

Moving beyond quiet stance: Applicability of the inverted pendulum model to stooping and crouching postures

“…Gaps in kinematic marker data were interpolated using a cubic spline routine (Howarth and Callaghan, 2010). Dual-pass, 2nd order digital Butterworth filters were used to filter the kinematic and kinetic data, both using a cut-off frequency of 1.2 Hz.…”

Hierarchical block-based incomplete human mocap data recovery using adaptive nonnegative matrix factorization

“…As the most commonly used methods, this type of approaches was favored for its fast computation and easy implementation. For instance, Rose et al [9] employed a radial basis function based interpolation scheme to create believable human articulations, while Howarth et al [10] selected different kinds of interpolation techniques (i.e., linear, cubic spline and local coordinate system) to achieve missing marker completion. In general, these types of interpolations requiring the incomplete data to be sampled before and after the available marker positions were often based on continuity in a single sequence, and thus may behave poorly when the markers were missing for a bit long period of time.…”