Inhomogeneous Template Adaptation of Temporal Quasi-Periodic Three-Dimensional Signals

We propose a novel framework for performing inhomogeneous template adaptation on temporal three-dimensional data. Templates of interest are first globally translated, scaled and rotated using statistical methods in the form of Procrustes analysis. In this globally adapted form, the data is parameterized to a minimum bounding rectangular prism using a parameterization method known as free-form deformation. The parameterized model is then reduced to a linear least squares problem, which subsequently allows for a robust local template adaptation scheme utilizing a non-parametric formulation. To account for complex noise behaviour encountered in quasi-periodic signals, we ensue to complete the framework with an adaptive noise model. We demonstrate the power of the proposed framework using three applications: firstly, we quantify the ability of the proposed framework to track geometric features of heart disease on a synthetic dataset containing three-dimensional electrocardiogram signals. Next we track these features on a publicly available real dataset (PTB database) to demonstrate its potential in heart disease diagnostics. Lastly, we apply the proposed framework to map three-dimensional velocity profiles using the publicly available Complex Upper-Limb Movements database.