DocumentCode :
34192
Title :
Automatic Fall Detection and Activity Classification by a Wearable Embedded Smart Camera
Author :
Ozcan, Koray ; Mahabalagiri, Anvith Katte ; Casares, Mauricio ; Velipasalar, Senem
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Syracuse Univ., Syracuse, NY, USA
Volume :
3
Issue :
2
fYear :
2013
fDate :
Jun-13
Firstpage :
125
Lastpage :
136
Abstract :
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.
Keywords :
assisted living; data privacy; edge detection; geriatrics; image classification; image sequences; object detection; patient diagnosis; patient monitoring; video cameras; video signal processing; CITRIC camera; CITRIC platform; activity classification; automatic fall detection; early health problem diagnosis; edge orientation histograms; edge strength histograms; elderly activity monitoring system; fall detection algorithm; fast fall detection; medical attention; nonfall activities; optical flow-based method; precise fall detection; privacy concerns; robust event detection; video sequences; wearable embedded smart camera; Activity classification; embedded; fall detection; histogram of oriented gradients; optical flow; smart cameras;
fLanguage :
English
Journal_Title :
Emerging and Selected Topics in Circuits and Systems, IEEE Journal on
Publisher :
ieee
ISSN :
2156-3357
Type :
jour
DOI :
10.1109/JETCAS.2013.2256832
Filename :
6507556
Link To Document :
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