Fall detection based on body part tracking using a depth camera

Zhen Peng Bian, Junhui Hou, Lap Pui Chau, Nadia Magnenat-Thalmann

Research output: Journal article publicationJournal articleAcademic researchpeer-review

210 Citations (Scopus)


The elderly population is increasing rapidly all over the world. One major risk for elderly people is fall accidents, especially for those living alone. In this paper, we propose a robust fall detection approach by analyzing the tracked key joints of the human body using a single depth camera. Compared to the rivals that rely on the RGB inputs, the proposed scheme is independent of illumination of the lights and can work even in a dark room. In our scheme, a pose-invariant randomized decision tree algorithm is proposed for the key joint extraction, which requires low computational cost during the training and test. Then, the support vector machine classifier is employed to determine whether a fall motion occurs, whose input is the 3-D trajectory of the head joint. The experimental results demonstrate that the proposed fall detection method is more accurate and robust compared with the state-of-the-art methods.

Original languageEnglish
Article number6804646
Pages (from-to)430-439
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Issue number2
Publication statusPublished - 1 Mar 2015
Externally publishedYes


  • 3-D
  • Computer vision
  • fall detection
  • head tracking
  • monocular
  • video surveillance

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management


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