Online classifier construction algorithm for human activity detection using a tri-axial accelerometer

Yen Ping Chen, Jhun Ying Yang, Shun Nan Liou, Gwo Yun Lee, Jeen-Shing Wang

Research output: Contribution to journalArticle

76 Citations (Scopus)

Abstract

This paper presents an online construction algorithm for constructing fuzzy basis function (FBF) classifiers that are capable of recognizing different types of human daily activities using a tri-axial accelerometer. The activity recognition is based on the acceleration data collected from a wireless tri-axial accelerometer module mounted on users' dominant wrists. Our objective is to enable users to: (1) online add new training samples to the existing classes for increasing the recognition accuracy, (2) online add additional classes to be recognized, and (3) online delete an existing class. For this objective we proposed a dynamic linear discriminant analysis (LDA) which can dynamically update the scatter matrices for online constructing FBF classifiers without storing all the training samples in memory. Our experimental results have successfully validated the integration of the FBF classifier with the proposed dynamic LDA can reduce computational burden and achieve satisfactory recognition accuracy.

Original languageEnglish
Pages (from-to)849-860
Number of pages12
JournalApplied Mathematics and Computation
Volume205
Issue number2
DOIs
Publication statusPublished - 2008 Nov 15

All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Online classifier construction algorithm for human activity detection using a tri-axial accelerometer'. Together they form a unique fingerprint.

Cite this