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dc.contributor.authorDanielsen, Asbjørn
dc.contributor.authorBremdal, Bernt Arild
dc.date.accessioned2018-05-09T12:26:25Z
dc.date.available2018-05-09T12:26:25Z
dc.date.issued2018-02-05
dc.description.abstractEach year about a third of the elderly aged 65 or older experience a fall. Many of these falls could be avoided if fall risk assessment and prevention tools where available in the daily living situation. Such tools would need to use the current context as input to predict an imminent fall. This paper presents an approach predicting imminent falls using data from a roof-mounted infrared array combined with an ultrasonic sensor. The data are processed and features extracted to determine location and posture along with indicators representing movement, direction, and velocity. These features are used by a classification algorithm to create a probability matrix representing the conditional probability of an individual in the current frame being recognized in a specific location and posture. A sequence of these probability matrices are fed into four artificial intelligence constructs trained to predict the probability of a future location/posture. The resulting conditional probability is used as a fall risk indicator to predict falls. Finally, the results from the experiment are presented. The study concludes that Elman Recurrent Neural Network with adapted Teacher Forcing has very promising properties and an explanation of the findings is offered.en_US
dc.descriptionEmbargoed OA due to IEEE regulations (manuscript version after 24 mnths embargo from publication date) Link to publisher's version: <a href=http://doi.org/10.1109/SSCI.2017.8280988>http://doi.org/10.1109/SSCI.2017.8280988</a>en_US
dc.identifier.citationDanielsen, A;Bremdal, B.A. Predicting Bedside Falls using Current Context. In: 2017 Symposium Series on Computational Intelligence. IEEE conference proceedings, p. 680-688en_US
dc.identifier.cristinIDFRIDAID 1538610
dc.identifier.doi10.1109/SSCI.2017.8280988
dc.identifier.isbn978-1-5386-2726-6
dc.identifier.urihttps://hdl.handle.net/10037/12713
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Technology: 500en_US
dc.titlePredicting Bedside Falls using Current Contexten_US
dc.typePeer revieweden_US
dc.typeChapteren_US
dc.typeBokkapittelno


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