Wearable computing: Accelerometers’ data classification of body postures and movements

W. Ugulino, D. Cardador, K. Vega, E. Velloso, R. Milidiú, H. Fuks

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

83 Citations (Scopus)

Abstract

During the last 5 years, research on Human Activity Recognition (HAR) has reported on systems showing good overall recognition performance. As a consequence, HAR has been considered as a potential technology for e-health systems. Here, we propose a machine learning based HAR classifier. We also provide a full experimental description that contains the HAR wearable devices setup and a public domain dataset comprising 165,633 samples. We consider 5 activity classes, gathered from 4 subjects wearing accelerometers mounted on their waist, left thigh, right arm, and right ankle. As basic input features to our classifier we use 12 attributes derived from a time window of 150ms. Finally, the classifier uses a committee AdaBoost that combines ten Decision Trees. The observed classifier accuracy is 99.4%.
Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence - SBIA 2012
Subtitle of host publication21th Brazilian Symposium on Artificial Intelligence, Curitiba, Brazil, October 20-25, 2012. Proceedings
PublisherSpringer
Pages52-61
ISBN (Electronic)978-3-642-34459-6
ISBN (Print)978-3-642-34458-9
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event21th Brazilian Symposium on Artificial Intelligence, SBIA 2012 - Curitiba, Brazil
Duration: 20 Oct 201225 Oct 2012
Conference number: 21

Publication series

Name
NameLecture Notes in Computer Science
Volume7589

Conference

Conference21th Brazilian Symposium on Artificial Intelligence, SBIA 2012
Abbreviated titleSBIA 2012
CountryBrazil
CityCuritiba
Period20/10/1225/10/12

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