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 :
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