Automatic fall detection and activity classification by a wearable embedded smart camera

Koray Ozcan, Anvith Katte Mahabalagiri, Mauricio Casares, Senem Velipasalar

Research output: Contribution to journalArticlepeer-review

85 Scopus citations


Robust detection of events and activities, such as falling, sitting, and lying down, is a key to a reliable elderly activity monitoring system. While fast and precise detection of falls is critical in providing immediate medical attention, other activities like sitting and lying down can provide valuable information for early diagnosis of potential health problems. In this paper, we present a fall detection and activity classification system using wearable cameras. Since the camera is worn by the subject, monitoring is not limited to confined areas, and extends to wherever the subject may go including indoors and outdoors. Furthermore, since the captured images are not of the subject, privacy concerns are alleviated. We present a fall detection algorithm employing histograms of edge orientations and strengths, and propose an optical flow-based method for activity classification. The first set of experiments has been performed with prerecorded video sequences from eight different subjects wearing a camera on their waist. Each subject performed around 40 trials, which included falling, sitting, and lying down. Moreover, an embedded smart camera implementation of the algorithm was also tested on a CITRIC platform with subjects wearing the CITRIC camera, and each performing 50 falls and 30 non-fall activities. Experimental results show the success of the proposed method.

Original languageEnglish (US)
Article number6507556
Pages (from-to)125-136
Number of pages12
JournalIEEE Journal on Emerging and Selected Topics in Circuits and Systems
Issue number2
StatePublished - 2013


  • Activity classification
  • embedded
  • fall detection
  • histogram of oriented gradients
  • optical flow
  • smart cameras

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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