Dominant pattern extraction from 3-D kinematic data

A new method for the extraction of a repeating pattern in cyclic biomechani cal data is proposed-singular value decomposition pattern analysis (SVDPA), This method is based on the recent work of Kanjilal and Palit [14], [15] a nd can be applied to both contiguous and repeated trials without being cons trained 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 m atrix with a special structure to identify repeating patterns. Several impo rtant features of SVDPA are described including its close relationship to t he Kahunen-Loeve 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 op timal [maximal signal-to-noise ratio (SNR)] ensemble average pattern. The d egree of periodicity and SNR for the AEC are defined explicitly from the si ngular values of the data matrix. We illustrate the usefulness of SVDPA for dominant pattern extraction by applying it to the quasiperiodic three-dime nsional trajectory of a marker attached to the trunk during treadmill locom otion. The AEC obtained for the normalized trajectory and error estimates a t each point suggests that SVDPA could be a useful tool for the extraction of the fine details from cyclic biomechanical data.