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dc.contributor.author Vanrell, Sebastián Rodrigo
dc.contributor.author Milone, Diego Humberto
dc.contributor.author Rufiner, Hugo Leonardo
dc.date.available 2018-06-06T19:54:43Z
dc.date.issued 2017-07
dc.identifier.citation Vanrell, Sebastián Rodrigo; Milone, Diego Humberto; Rufiner, Hugo Leonardo; Assessment of Homomorphic Analysis for Human Activity Recognition from Acceleration Signals; Institute of Electrical and Electronics Engineers; IEEE Journal of Biomedical and Health Informatics; 7-2017; 1-1
dc.identifier.issn 2168-2194
dc.identifier.uri http://hdl.handle.net/11336/47576
dc.description.abstract Unobtrusive activity monitoring can provide valuable information for medical and sports applications. In recent years, human activity recognition has moved to wearable sensors to deal with unconstrained scenarios. Accelerometers are the preferred sensors due to their simplicity and availability. Previous studies have examined several classic techniques for extracting features from acceleration signals, including time-domain, time-frequency, frequency-domain, and other heuristic features. Spectral and temporal features are the preferred ones and they are generally computed from acceleration components, leaving the acceleration magnitude potential unexplored. In this study, a new type of feature extraction stage, based on homomorphic analysis, is proposed in order to exploit discriminative activity information present in acceleration signals. Homomorphic analysis can isolate the information about whole body dynamics and translate it into a compact representation, called cepstral coefficients. Experiments have explored several configurations of the proposed features, including size of representation, signals to be used, and fusion with other features. Cepstral features computed from acceleration magnitude obtained one of the highest recognition rates. In addition, a beneficial contribution was found when time-domain and moving pace information was included in the feature vector. Overall, the proposed system achieved a recognition rate of 91.21% on the publicly available SCUT-NAA dataset. To the best of our knowledge, this is the highest recognition rate on this dataset.
dc.format application/pdf
dc.language.iso eng
dc.publisher Institute of Electrical and Electronics Engineers
dc.rights info:eu-repo/semantics/restrictedAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject HUMAN ACTIVITY RECOGNITION
dc.subject ACCELEROMETER
dc.subject SIGNAL PROCESSING
dc.subject CEPSTRUM
dc.subject.classification Ciencias de la Computación
dc.subject.classification Ciencias de la Computación e Información
dc.subject.classification CIENCIAS NATURALES Y EXACTAS
dc.title Assessment of Homomorphic Analysis for Human Activity Recognition from Acceleration Signals
dc.type info:eu-repo/semantics/article
dc.type info:ar-repo/semantics/artículo
dc.type info:eu-repo/semantics/publishedVersion
dc.date.updated 2018-05-31T18:18:54Z
dc.identifier.eissn 2168-2208
dc.journal.pagination 1-1
dc.journal.pais Estados Unidos
dc.journal.ciudad Nueva York
dc.description.fil Fil: Vanrell, Sebastián Rodrigo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
dc.description.fil Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
dc.description.fil Fil: Rufiner, Hugo Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
dc.journal.title IEEE Journal of Biomedical and Health Informatics
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/7967663/
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1109/JBHI.2017.2722870
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    Articulos de INST. DE INVESTIGACION EN SE?ALES, SISTEMAS E INTELIGENCIA COMPUTACIONAL

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info:eu-repo/semantics/restrictedAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)