A fall posture classification and recognition method based on wavelet packet transform and support vector machine

Qingyun Zhang, Jin Tao*, Qinglin Sun, Xianyi Zeng, Matthias Dehmer, Quan Zhou

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

An accidental fall seriously threatens the health and safety of the elderly. The injuries caused by a fall have a lot to do with the different postures during the fall. Therefore, recognizing the posture of falling is essential for the rescue and care of the elderly. In this paper, a novel method was proposed to improve the classification and recognition accuracy of fall postures. Firstly, the wavelet packet transform was used to extract multiple features from sample data. Secondly, random forest was used to evaluate the importance of the extracted features and obtain effective features through screening. Finally, the support vector machine classifier based on the linear kernel function was used to realize the falling posture recognition. The experiment results on “Simulated Falls and Daily Living Activities Data Set” show that the proposed method can distinguish different types of fall postures and achieve 99% classification accuracy.

Original languageEnglish
Article number5030
Number of pages14
JournalApplied Sciences (Switzerland)
Volume11
Issue number11
DOIs
Publication statusPublished - 29 May 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • Classification
  • Falling posture
  • Random forest
  • Recognition
  • Support vector machine
  • Wavelet packet transform

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