Dominant pattern extraction from 3-D kinematic data

A new method for the extraction of a repeating pattern in cyclic biomechanical data is proposed--singular value decomposition pattern analysis (SVDPA). This method is based on the recent work of Kanjilal and Palit [14], [15] and can be applied to both contiguous and repeated trials without being constrained to be strictly periodic. SVDPA is a data-driven approach that does not use a preselected set of basis functions; but instead utilizes a data matrix with a special structure to identify repeating patterns. Several important features of SVDPA are described including its close relationship to the Kahunen-Loève transform. The dominant pattern is defined as the average energy component (AEC). The AEC is obtained from the SVD of the data matrix and is equivalent to the optimal [maximal signal-to-noise ratio (SNR)] ensemble average pattern. The degree of periodicity and SNR for the AEC are defined explicitly from the singular values of the data matrix. We illustrate the usefulness of SVDPA for dominant pattern extraction by applying it to the quasiperiodic three-dimensional trajectory of a marker attached to the trunk during treadmill locomotion. The AEC obtained for the normalized trajectory and error estimates at each point suggests that SVDPA could be a useful tool for the extraction of the fine details from cyclic biomechanical data.