Exploiting the weakness of POF matched filter for the classification of motion data

In this paper, a classification method for classifying subjects' ability to follow some predefined trajectories is proposed using phase only filter (POF) [1]. In this research, we use three predefined trajectory patterns of different difficulty levels, and a set of data comprising four different classes of movements. We propose a POF to classify the data in those classes. POF can be assumed as a Complex Match Filter (CMF) where the amplitude of the object function is set to unity. The POF searches the entire image to find a match to the input filter. The trajectory in all three patterns contains edges and sharp turns which could considerably help to distinguish between the classes. Therefore, in this method, the reference pattern is segmented to several parts and critical segments of the trajectory used as an input filter or the pattern to search for. The classification task is applied for each pattern separately and the results obtained are fused based on different weights. The optimum weights for the fusion are obtained by using the training data and the linear regression technique.