Fall Detection Depth-Based Using Tilt Angle and Shape Deformation

View Sample PDF

Author(s): Fairouz Merrouche (University of Science and Technology USTHB, Algeria)and Nadia Baha (University of Science and Technology USTHB, Algeria)
Copyright: 2020
Pages: 17
Source title: Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-1204-3.ch039

Keywords: Data Analysis & Statistics / Library & Information Science / Medical Information Science Reference / Medical Technologies

Purchase

View Fall Detection Depth-Based Using Tilt Angle and Shape Deformation on the publisher's website for pricing and purchasing information.

Abstract

The population of elderly people is in growth. Falls risk their life, to disabilities, and to fears. Automatic fall detection systems provide them secure living; helping them to be independent at home. Computer vision offers efficient systems over many developed systems. In this article, the authors propose a new vision-based fall detection using depth camera. It combines human shape analysis, centroid detection and motion where it exploits the 3D information provided by a Kinect to compute the tilt angle to discriminate falls. Experimental tests were done with SDUFall dataset that contains 20 subjects performing five daily activities and falls, demonstrate the efficiency of the proposed system, and show that our method is promising achieving satisfactory results up to 84.66%.

The IRMA Community

Research IRM

Fall Detection Depth-Based Using Tilt Angle and Shape Deformation

Purchase

Abstract

Related Content

IRMA Sponsors