Show simple item record

dc.contributor.authorSamà Monsonís, Albert
dc.contributor.authorAngulo Bahón, Cecilio
dc.contributor.authorPardo Ayala, Diego Esteban
dc.contributor.authorCatalà Mallofré, Andreu
dc.contributor.authorCabestany Moncusí, Joan
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2011-09-13T07:27:39Z
dc.date.available2011-09-13T07:27:39Z
dc.date.issued2011-09
dc.identifier.issn0925-2312
dc.identifier.urihttp://hdl.handle.net/2117/13182
dc.description.abstractThis paper evaluates a set of computational algorithms for the automatic estimation of human postures and gait properties from signals provided by an inertial body sensor. The use of a single sensor device imposes limitations for the automatic estimation of relevant properties, like step length and gait velocity, as well as for the detection of standard postures like sitting or standing. Moreover, the exact location and orientation of the sensor is also a common restriction that is relaxed in this study. Based on accelerations provided by a sensor, known as the `9 2', three approaches are presented extracting kinematic information from the user motion and posture. Firstly, a two-phases procedure implementing feature extraction and Support Vector Machine based classi cation for daily living activity monitoring is presented. Secondly, Support Vector Regression is applied on heuristically extracted features for the automatic computation of spatiotemporal properties during gait. Finally, sensor information is interpreted as an observation of a particular trajectory of the human gait dynamical system, from which a reconstruction space is obtained, and then transformed using standard principal components analysis, nally Support Vector Regression is used for prediction. Daily living Activities are detected and spatiotemporal parameters of human gait are estimated using methods sharing a common structure based on feature extraction and kernel methods. The approaches presented are susceptible to be used for medical purposes.
dc.format.extent10 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Enginyeria electrònica::Instrumentació i mesura::Sensors i actuadors
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
dc.subject.lcshTime-series analysis
dc.subject.lcshGait in humans
dc.subject.lcshSupport vector machines
dc.subject.lcshPosture
dc.titleAnalyzing human gait and posture by combining feature selection and kernel methods
dc.typeArticle
dc.subject.lemacAlgorismes computacionals
dc.subject.lemacSèries temporals -- Anàlisi
dc.subject.lemacReconeixement de formes (Informàtica)
dc.subject.lemacPostura humana
dc.contributor.groupUniversitat Politècnica de Catalunya. GREC - Grup de Recerca en Enginyeria del Coneixement
dc.contributor.groupUniversitat Politècnica de Catalunya. AHA - Arquitectures Hardware Avançades
dc.identifier.doi10.1016/j.neucom.2011.03.028
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0925231211002475
dc.rights.accessOpen Access
local.identifier.drac5907596
dc.description.versionPreprint
local.personalitzacitaciotrue


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder