Analysis and classification of MoCap data by hilbert space embedding-based distance and multikernel learning

A framework is presented to carry out prediction and classification of Motion Capture (MoCap) multichannel data, based on kernel adaptive filters and multi-kernel learning. To this end, a Kernel Adaptive Filter (KAF) algorithm extracts the dynamic of each channel, relying on the similarity between multiple realizations through the Maximum Mean Discrepancy (MMD) criterion. To assemble dynamics extracted from all MoCap data, center kernel alignment (CKA) is used to assess the contribution of each to the classification tasks (that is, its relevance). Validation is performed on a database of tennis players, performing a good classification accuracy of the considered stroke classes. Besides, we find that the relevance of each channel agrees with the findings reported in the biomechanical analysis. Therefore, the combination of KAF together with CKA allows building a proper representation for extracting relevant dynamics from multiple-channel MoCap dataThis work is supported by the project 36075 and mobility grant 8401 funded by Universidad Nacional de Colombia sede Manizales, by program “Doctorados Nacionales 2014” number 647 funded by COLCIENCIAS, as well as PhD financial support from Universidad Autónoma de Occident