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dc.contributor.authorBelmonte Klein, Fen
dc.contributor.authorCangelosi, ACen
dc.contributor.authorŠtěpánová, KSen
dc.contributor.editorHirose, AHen
dc.contributor.editorOzawa, OSen
dc.contributor.editorDoya, KDen
dc.contributor.editorKazushi, KIen
dc.contributor.editorMinho, MLen
dc.contributor.editorDerong, DLen
dc.date.accessioned2017-06-20T15:14:24Z
dc.date.accessioned2017-06-20T15:21:08Z
dc.date.available2017-06-20T15:14:24Z
dc.date.available2017-06-20T15:21:08Z
dc.date.issued2016-09-30en
dc.identifier.isbn3319466879en
dc.identifier.isbn9783319466873en
dc.identifier.urihttp://hdl.handle.net/10026.1/9502
dc.description.abstract

In this paper we aim for the replication of a state of the art architecture for recognition of human actions using skeleton poses obtained from a depth sensor. We review the usefulness of accurate human action recognition in the field of robotic elderly care, focusing on fall detection. We attempt fall recognition using a chained Growing When Required neural gas classifier that is fed only skeleton joints data. We test this architecture against Recurrent SOMs (RSOMs) to classify the TST Fall detection database ver. 2, a specialised dataset for fall sequences. We also introduce a simplified mathematical model of falls for easier and faster bench-testing of classification algorithms for fall detection. The outcome of classifying falls from our mathematical model was successful with an accuracy of 97.12±1.65% and from the TST Fall detection database ver. 2 with an accuracy of 90.2±2.68% when a filter was added.

en
dc.format.extent526 - 534 (9)en
dc.language.isoenen
dc.publisherSpringeren
dc.relation.ispartofNeural Information Processing 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedingsen
dc.relation.replaceshttp://hdl.handle.net/10026.1/9501
dc.relation.replaces10026.1/9501
dc.subjectComputersen
dc.subjectfall detectionen
dc.titleImplementation of a Modular Growing When Required Neural Gas Architecture for Recognition of Fallsen
dc.typeBook Chapter
plymouth.volume9947en
plymouth.publication-statusPublisheden
plymouth.seriesLNCSen
dc.identifier.doi10.1007/978-3-319-46687-3_58en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Research Groups
plymouth.organisational-group/Plymouth/Research Groups/Institute of Health and Community
plymouth.organisational-group/Plymouth/Research Groups/Marine Institute
dc.rights.embargoperiod12 monthsen
rioxxterms.versionofrecord10.1007/978-3-319-46687-3_58en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/under-embargo-all-rights-reserveden
rioxxterms.typeBook chapteren


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