Autoregressive modeling of wrist attitude for feature enrichment in human activity recognition

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dc.creatorPriscila L. R. Aguirre
dc.creatorLeonardo Antônio Borges Tôrres
dc.creatorAndré Paim Lemos
dc.date.accessioned2025-04-02T15:36:28Z
dc.date.accessioned2025-09-09T00:58:22Z
dc.date.available2025-04-02T15:36:28Z
dc.date.issued2017
dc.identifier.doi10.21528/CBIC2017-72
dc.identifier.urihttps://hdl.handle.net/1843/81227
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofXIII Congresso Brasileiro de Inteligência Computacional – CBIC
dc.rightsAcesso Aberto
dc.subjectInteligência artificial
dc.subjectAprendizado do computador
dc.subjectRepresentação do conhecimento (Teoria da informação)
dc.subject.otherhuman activity recognition
dc.subject.otherAutoregressive models
dc.subject.otherSVM
dc.subject.otherwrist attitude
dc.titleAutoregressive modeling of wrist attitude for feature enrichment in human activity recognition
dc.typeArtigo de evento
local.citation.epage12
local.citation.spage1
local.description.resumoThe use of time-series from wrist worn accelerometers for Human Activity Recognition is investigated in this work. We employ, as features, coefficients of two-dimensional multivariate/vector autoregressive (AR) models obtained from raw acceleration signals and from estimated wrist attitude roll and pitch angles. It is shown that the simultaneous use of both types of models improves the overall accuracy about 20% when compared to recently published algorithms where only univariate AR models coefficients for each raw acceleration signal are employed.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://sbic.org.br/eventos/cbic_2017/cbic-paper-72/

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