Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers

Jhun Ying Yang, Jeen-Shing Wang, Yen Ping Chen

Research output: Contribution to journalArticlepeer-review

257 Citations (Scopus)

Abstract

This paper presents a systematic design approach for constructing neural classifiers that are capable of classifying human activities using a triaxial accelerometer. The philosophy of our design approach is to apply a divide-and-conquer strategy that separates dynamic activities from static activities preliminarily and recognizes these two different types of activities separately. Since multilayer neural networks can generate complex discriminating surfaces for recognition problems, we adopt neural networks as the classifiers for activity recognition. An effective feature subset selection approach has been developed to determine significant feature subsets and compact classifier structures with satisfactory accuracy. Experimental results have successfully validated the effectiveness of the proposed recognition scheme.

Original languageEnglish
Pages (from-to)2213-2220
Number of pages8
JournalPattern Recognition Letters
Volume29
Issue number16
DOIs
Publication statusPublished - 2008 Dec 1

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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