A set of general sensors that can be fit the data acquisition strategy to distinguish a fall from a non-fall.
Published in Chapter:
Fall Detection Systems to be Used by Elderly People
Getúlio Igrejas (ESTiG, Polytechnic Institute of Bragança, Portugal), Joana S. Amaral (REQUIMTE, Faculty of Pharmacy, University of Porto & ESTiG, Polytechnic Institute of Bragança, Portugal), and Pedro J. S. Rodrigues (ESTiG, Polytechnic Institute of Bragança, Portugal)
Copyright: © 2013
|Pages: 25
DOI: 10.4018/978-1-4666-3986-7.ch024
Abstract
Statistics show that, each year, falls affect tens of millions of elderly people throughout the world. Falls are the leading cause of injury deaths and injury-related hospitalization among people over 65 years old. A system able to automatically detect falls could be an important tool from a social point of view, as it would contribute to the prompt assistance of these emergency cases. Currently, many researchers are interested in the development of fall detection systems. This chapter presents several approaches suggested so far, with special attention to the different strategies and technologies applied. Other sensor technologies that could be applied to this field are also referred. Additionally, a new fall detection system based on a machine learning paradigm, using neural networks, is suggested. The system was trained using fall and non-fall examples. Although the test set has included some particular examples, problematic for detection of the correspondent motion, the obtained results present good specificity (88.9%) and sensitivity (93.9%) rates.