Fall detection based on body part tracking using a depth camera

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

142 Scopus Citations
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Author(s)

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

Detail(s)

Original languageEnglish
Article number6804646
Pages (from-to)430-439
Journal / PublicationIEEE Journal of Biomedical and Health Informatics
Volume19
Issue number2
Publication statusPublished - 1 Mar 2015
Externally publishedYes

Abstract

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.

Research Area(s)

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

Citation Format(s)

Fall detection based on body part tracking using a depth camera. / Bian, Zhen-Peng; Hou, Junhui; Chau, Lap-Pui; Magnenat-Thalmann, Nadia.

In: IEEE Journal of Biomedical and Health Informatics, Vol. 19, No. 2, 6804646, 01.03.2015, p. 430-439.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review