A fall detection system using k-nearest neighbor classifier

Chien-Liang Liu*, Chia-Hoang Lee, Ping Min Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

161 Scopus citations


The main purpose of this paper is to use off-the-shelf devices to develop a fall detection system. In human body identification, human body silhouette is adopted to improve privacy protection, and vertical projection histograms of the silhouette image and statistical scheme are used to reduce the effect of human body upper limb activities. The kNN classification algorithm is used to classify the postures using the ratio and difference of human body silhouette bounding box height and width. Meanwhile, since time difference is a vital factor to differentiate fall incident event and lying down event, the critical time difference is obtained from the experiment and verified by statistical hypothesis testing. With the help of the kNN classifier and the critical time difference, a fall incident detection system is developed to detect fall incident events. The experiment shows that it could reduce the effect of upper limb activities and the system has a correct rate of 84.44% on fall detection and lying down event detection.

Original languageAmerican English
Pages (from-to)7174-7181
Number of pages8
JournalExpert Systems with Applications
Issue number10
StatePublished - Oct 2010


  • Artificial intelligence
  • Fall detection
  • Machine learning
  • kNN


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