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- W3042712658 abstract "In the context of Ubiquitous Sensing, a field that investigates how to extract knowledgeusing pervasive sensors, the recognition of human activities is a task of greatinterest in surveillance, healthcare and entertainment systems. Although it has beenexplored for more than a decade, many issues, such as the collection of data underrealistic conditions, the flexibility to support new users, implementations that meetprocessing and energy consumption requirements, etc, still motivate the developmentof new techniques in order to increase performance.To the best of our knowledge, most researches consider recognition of activities thatinvolve postural transitions, which are categorized as ambulation activities. Motivatedmostly by the lack of studies which consider activities classified as daily living, whichare more complex when compared with ambulation activities, this work investigatesfeature enrichment from wrist worn accelerometer data in order to refine the abilityof recognizing such type of human activities, which tend to be rich in wrist and armmovement.Based on the assumption that wrist orientation contains useful information thatcould increase recognition of daily life activities, we investigate the e ects of applyinga non-linear mathematical transformation to the tri-axial accelerometer signals in orderto estimate wrist attitude roll and pitch angles. We employ, as features, coe cients oftwo-dimensional multivariate/vector autoregressive (AR) models obtained from rawacceleration signals and from estimated wrist attitude angles. It is shown that thesimultaneous use of both types of models improves the overall accuracy about 10%when compared to recently published algorithms where only univariate AR modelscoe cients for each raw acceleration signal are employed." @default.
- W3042712658 created "2020-07-23" @default.
- W3042712658 creator A5090731783 @default.
- W3042712658 date "2018-04-02" @default.
- W3042712658 modified "2023-09-27" @default.
- W3042712658 title "Feature enrichment in human activity recognition" @default.
- W3042712658 hasPublicationYear "2018" @default.
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