Eigenfoot decomposition of plantar pressure images and case study of feature prediction of two modalities

The registration of plantar pressure images is a widely used technique to support human gait analysis. In plantar pressure images, most of the time conventionally derived features are used for further processing. Recently, automatic feature extraction based on PCA and kPCA is being used, to increase the information that can be extracted from this data. In this paper, we describe our work flow and a case study on the application of predicting two pressure features and a non-pressure feature out of the automatically derived PCA features. This includes the normalization of the pressure images, the PCA based feature extraction, and building and testing the regression model based on a linear and kernel SVM.