Título: Autoregressive Modeling of Wrist Attitude for Feature Enrichment in Human Activity Recognition
Autores: Aguirre, Priscila
The 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.
human activity recognition;wrist attitude;autoregressive models;SVM
Código DOI: 10.21528/CBIC2017-72
Artigo em pdf: cbic-paper-72.pdf
Arquivo BibTeX: cbic-paper-72.bib