Advisor(s)
Abstract(s)
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 sen-
sor 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.
Description
Keywords
Fall detector Neural networks Machine learning Elderly people
Citation
Igrejas, Getúlio; Amaral, J.S.; Rodrigues, P.J.S. (2013). Fall detection systems to be used by elderly people. In Cruz-Cunha, Maria Manuela [et al.] Handbook of Research on ICTs for Human-Centered Healthcare and Social Care Services. IGI Global. p.449-473. ISBN 9781466639867