DocumentCode
636362
Title
Artificial Neural Networks as an alternative to traditional fall detection methods
Author
Vallejo, Monica ; Isaza, Claudia V. ; Lopez, Jose D.
Author_Institution
Dept. of Electron. Eng., Univ. de Antioquia, Medellin, Colombia
fYear
2013
fDate
3-7 July 2013
Firstpage
1648
Lastpage
1651
Abstract
Falls are common events among older adults and may have serious consequences. Automatic fall detection systems are becoming a popular tool to rapidly detect such events, helping family or health personal to rapidly help the person that falls. This paper presents the results obtained in the process of testing a new fall detection method, based on Artificial Neural Networks (ANN). This method intends to improve fall detection accuracy, by avoiding the traditional threshold - based fall detection methods, and introducing ANN as a suitable option on this application.Also ANN have low computational cost, this characteristic makes them easy to implement on a portable device, comfortable to be wear by the patient.
Keywords
biomedical equipment; geriatrics; medical signal detection; medical signal processing; neural nets; portable instruments; ANN; artificial neural networks; automatic fall detection systems; fall detection accuracy; older adults; portable device; traditional fall detection methods; Acceleration; Accelerometers; Artificial neural networks; Biomedical monitoring; Neurons; Sensors; Training;
fLanguage
English
Publisher
ieee
Conference_Titel
Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
Conference_Location
Osaka
ISSN
1557-170X
Type
conf
DOI
10.1109/EMBC.2013.6609833
Filename
6609833
Link To Document