Implementation of a Modular Growing When Required Neural Gas Architecture for Recognition of Falls
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00306982" target="_blank" >RIV/68407700:21230/16:00306982 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007%2F978-3-319-46687-3_58" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-319-46687-3_58</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-46687-3_58" target="_blank" >10.1007/978-3-319-46687-3_58</a>
Alternative languages
Result language
angličtina
Original language name
Implementation of a Modular Growing When Required Neural Gas Architecture for Recognition of Falls
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part I
ISBN
978-3-319-46686-6
ISSN
0302-9743
e-ISSN
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Number of pages
9
Pages from-to
526-534
Publisher name
Springer International Publishing
Place of publication
Cham
Event location
Kyoto
Event date
Oct 16, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000389805900058