A time series classification approach for motion analysis using ensembles in Ubiquitous healthcare systems

Rana Salaheldin, Mohamed Elhelw, Neamat El Gayar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Human motion analysis is a vital research area for healthcare systems. The increasing need for automated activity analysis inspired the design of low cost wireless sensors that can capture information under free living conditions. Body and Visual Sensor Networks can easily record human behavior within a home environment. In this paper we propose a multiple classifier system that uses time series data for human motion analysis. The proposed approach adaptively integrates feature extraction and distance based techniques for classifying impaired and normal walking gaits. Information from body sensors and multiple vision nodes are used to extract local and global features. Our proposed method is tested against various classifiers trained using different feature spaces. The results for the different training schemes are presented. We demonstrate that the proposed model outperforms the other presented classification methods.

Original languageEnglish
Title of host publicationArtificial Neural Networks in Pattern Recognition. ANNPR 2014
EditorsNeamat El Gayar, Friedhelm Schwenker, Ching Y. Suen
PublisherSpringer
Pages277-288
Number of pages12
ISBN (Electronic)9783319116563
ISBN (Print)9783319116556
DOIs
Publication statusPublished - 2014
Event6th IAPR TC3 International Workshop on Artificial Neural Networks for Pattern Recognition 2014 - Montreal, Canada
Duration: 6 Oct 20148 Oct 2014

Publication series

NameLecture Notes in Computer Science
Volume8774
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th IAPR TC3 International Workshop on Artificial Neural Networks for Pattern Recognition 2014
Abbreviated titleANNPR 2014
CountryCanada
CityMontreal
Period6/10/148/10/14

Keywords

  • Human motion analysis
  • Multiple classifier systems
  • Time series classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Salaheldin, R., Elhelw, M., & Gayar, N. E. (2014). A time series classification approach for motion analysis using ensembles in Ubiquitous healthcare systems. In N. E. Gayar, F. Schwenker, & C. Y. Suen (Eds.), Artificial Neural Networks in Pattern Recognition. ANNPR 2014 (pp. 277-288). (Lecture Notes in Computer Science; Vol. 8774). Springer. https://doi.org/10.1007/978-3-319-11656-3_25