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 \( 97.12 \pm 1.65\,\%\) and from the TST Fall detection database ver. 2 with an accuracy of \(90.2 \pm 2.68\,\%\) when a filter was added.
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Acknowledgment
This work was partially supported by CNPq Brazil (scholarship 232590/2014-1) and by SGS grant No. 10/279/OHK3/3T/13, sponsored by the CTU in Prague, Czech Republic.
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Klein, F.B., Štěpánová, K., Cangelosi, A. (2016). Implementation of a Modular Growing When Required Neural Gas Architecture for Recognition of Falls. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9947. Springer, Cham. https://doi.org/10.1007/978-3-319-46687-3_58
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