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 data

• Autores: Pulgarín Giraldo, Juan Diego; Álvarez Meza, Andrés Marino; Santamaría, Ignacio; Van Vaerenbergh, Steven; Castellanos Dominguez, Germán
• Tipo de recurso: Article of journal
• Fecha de publicación: 2019
• Institución: Universidad Autónoma de Occidente
• Repositorio: RED: Repositorio Educativo Digital UAO
• Idioma: eng
• OAI Identifier: oai:red.uao.edu.co:10614/11493
• Acceso en línea: http://hdl.handle.net/10614/11493; https://doi.org/10.1007/978-3-030-13469-3_22
• Palabra clave: Análisis funcional; Functional analysis; Filtros eléctricos; Electric filters
• Rights: restrictedAccess
• License: Derechos Reservados - Universidad Autónoma de Occidente