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 m...

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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
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repository_id_str
dc.title.eng.fl_str_mv Analysis and classification of MoCap data by hilbert space embedding-based distance and multikernel learning
title Analysis and classification of MoCap data by hilbert space embedding-based distance and multikernel learning
spellingShingle Analysis and classification of MoCap data by hilbert space embedding-based distance and multikernel learning
Análisis funcional
Functional analysis
Filtros eléctricos
Electric filters
title_short Analysis and classification of MoCap data by hilbert space embedding-based distance and multikernel learning
title_full Analysis and classification of MoCap data by hilbert space embedding-based distance and multikernel learning
title_fullStr Analysis and classification of MoCap data by hilbert space embedding-based distance and multikernel learning
title_full_unstemmed Analysis and classification of MoCap data by hilbert space embedding-based distance and multikernel learning
title_sort Analysis and classification of MoCap data by hilbert space embedding-based distance and multikernel learning
dc.creator.fl_str_mv Pulgarín Giraldo, Juan Diego
Álvarez Meza, Andrés Marino
Santamaría, Ignacio
Van Vaerenbergh, Steven
Castellanos Dominguez, Germán
dc.contributor.author.none.fl_str_mv Pulgarín Giraldo, Juan Diego
Álvarez Meza, Andrés Marino
Santamaría, Ignacio
Van Vaerenbergh, Steven
Castellanos Dominguez, Germán
dc.subject.lemb.spa.fl_str_mv Análisis funcional
topic Análisis funcional
Functional analysis
Filtros eléctricos
Electric filters
dc.subject.lemb.eng.fl_str_mv Functional analysis
dc.subject.armarc.spa.fl_str_mv Filtros eléctricos
dc.subject.armarc.eng.fl_str_mv Electric filters
description 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
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-11-14T16:47:25Z
dc.date.available.none.fl_str_mv 2019-11-14T16:47:25Z
dc.date.issued.none.fl_str_mv 2019-03-03
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.isbn.spa.fl_str_mv 978-3-030-13469-3 (en línea)
dc.identifier.issn.spa.fl_str_mv 978-3-030-13468-6 (impresa)
dc.identifier.uri.spa.fl_str_mv http://hdl.handle.net/10614/11493
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/978-3-030-13469-3_22
identifier_str_mv 978-3-030-13469-3 (en línea)
978-3-030-13468-6 (impresa)
url http://hdl.handle.net/10614/11493
https://doi.org/10.1007/978-3-030-13469-3_22
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.eng.fl_str_mv
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dc.relation.cites.eng.fl_str_mv Pulgarin-Giraldo J.D., Alvarez-Meza A.M., Van Vaerenbergh S., Santamaría I., Castellanos-Dominguez G. (2019) Analysis and Classification of MoCap Data by Hilbert Space Embedding-Based Distance and Multikernel Learning. In: Vera-Rodriguez R., Fierrez J., Morales A. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2018. Lecture Notes in Computer Science, vol 11401. Springer, Cham. https://doi.org/10.1007/978-3-030-13469-3_22
dc.relation.ispartofjournal.eng.fl_str_mv Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. 23rd Iberoamerican Congress, CIARP 2018, Madrid, Spain, November 19-22, 2018, Proceedings
dc.relation.references.none.fl_str_mv 1. Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., Bajcsy, R.: Sequence of the most informative joints (SMIJ): a new representation for human skeletal action recognition. J. Vis. Commun. Image Represent. 25(1), 24–38 (2014) CrossRefGoogle Scholar
2. Van Vaerenbergh, S., Santamaría, I.: A comparative study of kernel adaptive filtering algorithms. In: 2013 IEEE DSP/SPE Meeting, pp. 181–186, August 2013. Software available at https://github.com/steven2358/kafbox/
3. Pulgarin-Giraldo, J.D., Alvarez-Meza, A.M., Melo-Betancourt, L.G., Ramos-Bermudez, S., Castellanos-Dominguez, G.: A similarity indicator for differentiating kinematic performance between qualified tennis players. In: Beltrán-Castañón, C., Nyström, I., Famili, F. (eds.) CIARP 2016. LNCS, vol. 10125, pp. 309–317. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52277-7_38 CrossRefGoogle Scholar
4. Cortes, C., Mohri, M., Rostamizadeh, A.: Algorithms for learning kernels based on centered alignment. J. Mach. Learn. Res. 13(1), 795–828 (2012) MathSciNetzbMATHGoogle Scholar
5. Van Vaerenbergh, S., Lazaro-Gredilla, M., Santamaria, I.: Kernel recursive least-squares tracker for time-varying regression. IEEE Trans. Neural Netw. Learn. Syst. 23(8), 1313–1326 (2012) CrossRefGoogle Scholar
6. Gretton, A., Borgwardt, K.M., Rasch, M.J., Schölkopf, B., Smola, A.: A kernel two-sample test. J. Mach. Learn. Res. 13, 723–773 (2012) MathSciNetzbMATHGoogle Scholar
7. Álvarez, M.A., Rosasco, L., Lawrence, N.D.: Kernels for vector-valued functions: a review. Found. Trends Mach. Learn. 4(3), 195–266 (2012) CrossRefGoogle Scholar
8. Landlinger, J., Lindinger, S., Stoggl, T., Wagner, H., Muller, E.: Key factors and timing patterns in the tennis forehand of different skill levels. J. Sports Sci. Med. 9, 643–651 (2010) Google Scholar
dc.rights.spa.fl_str_mv Derechos Reservados - Universidad Autónoma de Occidente
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spelling Pulgarín Giraldo, Juan Diegovirtual::4147-1Álvarez Meza, Andrés Marino7fd52c5e946073a9aac3ed6f493759d7Santamaría, Ignacioc228ad487332d586f2907de1a6d84b30Van Vaerenbergh, Stevenb595d1d26fe70b490d7a8c014323f9ceCastellanos Dominguez, Germánc696584d36aa6e916ebcbb469f81affeUniversidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí2019-11-14T16:47:25Z2019-11-14T16:47:25Z2019-03-03978-3-030-13469-3 (en línea)978-3-030-13468-6 (impresa)http://hdl.handle.net/10614/11493https://doi.org/10.1007/978-3-030-13469-3_22A 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 Occidenteapplication/pdf8 páginasengSpringer, Cham19318611401Pulgarin-Giraldo J.D., Alvarez-Meza A.M., Van Vaerenbergh S., Santamaría I., Castellanos-Dominguez G. (2019) Analysis and Classification of MoCap Data by Hilbert Space Embedding-Based Distance and Multikernel Learning. In: Vera-Rodriguez R., Fierrez J., Morales A. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2018. Lecture Notes in Computer Science, vol 11401. Springer, Cham. https://doi.org/10.1007/978-3-030-13469-3_22Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. 23rd Iberoamerican Congress, CIARP 2018, Madrid, Spain, November 19-22, 2018, Proceedings1. Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., Bajcsy, R.: Sequence of the most informative joints (SMIJ): a new representation for human skeletal action recognition. J. Vis. Commun. Image Represent. 25(1), 24–38 (2014) CrossRefGoogle Scholar2. Van Vaerenbergh, S., Santamaría, I.: A comparative study of kernel adaptive filtering algorithms. In: 2013 IEEE DSP/SPE Meeting, pp. 181–186, August 2013. Software available at https://github.com/steven2358/kafbox/3. Pulgarin-Giraldo, J.D., Alvarez-Meza, A.M., Melo-Betancourt, L.G., Ramos-Bermudez, S., Castellanos-Dominguez, G.: A similarity indicator for differentiating kinematic performance between qualified tennis players. In: Beltrán-Castañón, C., Nyström, I., Famili, F. (eds.) CIARP 2016. LNCS, vol. 10125, pp. 309–317. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52277-7_38 CrossRefGoogle Scholar4. Cortes, C., Mohri, M., Rostamizadeh, A.: Algorithms for learning kernels based on centered alignment. J. Mach. Learn. Res. 13(1), 795–828 (2012) MathSciNetzbMATHGoogle Scholar5. Van Vaerenbergh, S., Lazaro-Gredilla, M., Santamaria, I.: Kernel recursive least-squares tracker for time-varying regression. IEEE Trans. Neural Netw. Learn. Syst. 23(8), 1313–1326 (2012) CrossRefGoogle Scholar6. Gretton, A., Borgwardt, K.M., Rasch, M.J., Schölkopf, B., Smola, A.: A kernel two-sample test. J. Mach. Learn. Res. 13, 723–773 (2012) MathSciNetzbMATHGoogle Scholar7. Álvarez, M.A., Rosasco, L., Lawrence, N.D.: Kernels for vector-valued functions: a review. Found. Trends Mach. Learn. 4(3), 195–266 (2012) CrossRefGoogle Scholar8. Landlinger, J., Lindinger, S., Stoggl, T., Wagner, H., Muller, E.: Key factors and timing patterns in the tennis forehand of different skill levels. J. Sports Sci. Med. 9, 643–651 (2010) Google ScholarDerechos Reservados - Universidad Autónoma de Occidentehttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_16echttps://link.springer.com/chapter/10.1007/978-3-030-13469-3_22reponame:Repositorio Institucional UAOAnalysis and classification of MoCap data by hilbert space embedding-based distance and multikernel learningArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTREFinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Análisis funcionalFunctional analysisFiltros eléctricosElectric filtersPublication33e9b6b4-bd6d-4b86-b500-ae237e1e9a98virtual::4147-133e9b6b4-bd6d-4b86-b500-ae237e1e9a98virtual::4147-1https://scholar.google.com.co/citations?user=Bwuc2BkAAAAJ&hl=envirtual::4147-10000-0002-6409-5104virtual::4147-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000207497virtual::4147-1CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://red.uao.edu.co/bitstreams/85944a43-ff5e-4a08-b553-dce9c94cafa9/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81665https://red.uao.edu.co/bitstreams/c9fadbcb-85bf-4892-ac95-acdae97b9c88/download20b5ba22b1117f71589c7318baa2c560MD5310614/11493oai:red.uao.edu.co:10614/114932024-03-13 14:20:42.624https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos Reservados - Universidad Autónoma de Occidentemetadata.onlyhttps://red.uao.edu.coRepositorio Digital Universidad Autonoma de Occidenterepositorio@uao.edu.coRUwgQVVUT1IgYXV0b3JpemEgYSBsYSBVbml2ZXJzaWRhZCBBdXTDs25vbWEgZGUgT2NjaWRlbnRlLCBkZSBmb3JtYSBpbmRlZmluaWRhLCBwYXJhIHF1ZSBlbiBsb3MgdMOpcm1pbm9zIGVzdGFibGVjaWRvcyBlbiBsYSBMZXkgMjMgZGUgMTk4MiwgbGEgTGV5IDQ0IGRlIDE5OTMsIGxhIERlY2lzacOzbiBhbmRpbmEgMzUxIGRlIDE5OTMsIGVsIERlY3JldG8gNDYwIGRlIDE5OTUgeSBkZW3DoXMgbGV5ZXMgeSBqdXJpc3BydWRlbmNpYSB2aWdlbnRlIGFsIHJlc3BlY3RvLCBoYWdhIHB1YmxpY2FjacOzbiBkZSBlc3RlIGNvbiBmaW5lcyBlZHVjYXRpdm9zLiBQQVJBR1JBRk86IEVzdGEgYXV0b3JpemFjacOzbiBhZGVtw6FzIGRlIHNlciB2w6FsaWRhIHBhcmEgbGFzIGZhY3VsdGFkZXMgeSBkZXJlY2hvcyBkZSB1c28gc29icmUgbGEgb2JyYSBlbiBmb3JtYXRvIG8gc29wb3J0ZSBtYXRlcmlhbCwgdGFtYmnDqW4gcGFyYSBmb3JtYXRvIGRpZ2l0YWwsIGVsZWN0csOzbmljbywgdmlydHVhbCwgcGFyYSB1c29zIGVuIHJlZCwgSW50ZXJuZXQsIGV4dHJhbmV0LCBpbnRyYW5ldCwgYmlibGlvdGVjYSBkaWdpdGFsIHkgZGVtw6FzIHBhcmEgY3VhbHF1aWVyIGZvcm1hdG8gY29ub2NpZG8gbyBwb3IgY29ub2Nlci4gRUwgQVVUT1IsIGV4cHJlc2EgcXVlIGVsIGRvY3VtZW50byAodHJhYmFqbyBkZSBncmFkbywgcGFzYW50w61hLCBjYXNvcyBvIHRlc2lzKSBvYmpldG8gZGUgbGEgcHJlc2VudGUgYXV0b3JpemFjacOzbiBlcyBvcmlnaW5hbCB5IGxhIGVsYWJvcsOzIHNpbiBxdWVicmFudGFyIG5pIHN1cGxhbnRhciBsb3MgZGVyZWNob3MgZGUgYXV0b3IgZGUgdGVyY2Vyb3MsIHkgZGUgdGFsIGZvcm1hLCBlbCBkb2N1bWVudG8gKHRyYWJham8gZGUgZ3JhZG8sIHBhc2FudMOtYSwgY2Fzb3MgbyB0ZXNpcykgZXMgZGUgc3UgZXhjbHVzaXZhIGF1dG9yw61hIHkgdGllbmUgbGEgdGl0dWxhcmlkYWQgc29icmUgw6lzdGUuIFBBUkFHUkFGTzogZW4gY2FzbyBkZSBwcmVzZW50YXJzZSBhbGd1bmEgcmVjbGFtYWNpw7NuIG8gYWNjacOzbiBwb3IgcGFydGUgZGUgdW4gdGVyY2VybywgcmVmZXJlbnRlIGEgbG9zIGRlcmVjaG9zIGRlIGF1dG9yIHNvYnJlIGVsIGRvY3VtZW50byAoVHJhYmFqbyBkZSBncmFkbywgUGFzYW50w61hLCBjYXNvcyBvIHRlc2lzKSBlbiBjdWVzdGnDs24sIEVMIEFVVE9SLCBhc3VtaXLDoSBsYSByZXNwb25zYWJpbGlkYWQgdG90YWwsIHkgc2FsZHLDoSBlbiBkZWZlbnNhIGRlIGxvcyBkZXJlY2hvcyBhcXXDrSBhdXRvcml6YWRvczsgcGFyYSB0b2RvcyBsb3MgZWZlY3RvcywgbGEgVW5pdmVyc2lkYWQgIEF1dMOzbm9tYSBkZSBPY2NpZGVudGUgYWN0w7phIGNvbW8gdW4gdGVyY2VybyBkZSBidWVuYSBmZS4gVG9kYSBwZXJzb25hIHF1ZSBjb25zdWx0ZSB5YSBzZWEgZW4gbGEgYmlibGlvdGVjYSBvIGVuIG1lZGlvIGVsZWN0csOzbmljbyBwb2Ryw6EgY29waWFyIGFwYXJ0ZXMgZGVsIHRleHRvIGNpdGFuZG8gc2llbXByZSBsYSBmdWVudGUsIGVzIGRlY2lyIGVsIHTDrXR1bG8gZGVsIHRyYWJham8geSBlbCBhdXRvci4gRXN0YSBhdXRvcml6YWNpw7NuIG5vIGltcGxpY2EgcmVudW5jaWEgYSBsYSBmYWN1bHRhZCBxdWUgdGllbmUgRUwgQVVUT1IgZGUgcHVibGljYXIgdG90YWwgbyBwYXJjaWFsbWVudGUgbGEgb2JyYS4K

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