dc.contributor.author | Casagrande, Flavia Dias | |
dc.contributor.author | Nedrejord, Oda Olsen | |
dc.contributor.author | Lee, Wonho | |
dc.contributor.author | Zouganeli, Evi | |
dc.date.accessioned | 2020-01-10T12:54:35Z | |
dc.date.accessioned | 2020-04-08T11:41:46Z | |
dc.date.available | 2020-01-10T12:54:35Z | |
dc.date.available | 2020-04-08T11:41:46Z | |
dc.date.issued | 2019-08-05 | |
dc.identifier.citation | Casagrande FDC, Nedrejord OO, Lee W, Zouganeli E P. Action Recognition in Real Homes using Low Resolution Depth Video Data . Computer-Based Medical Systems. 2019;2019-June:156-161 | en |
dc.identifier.issn | 1063-7125 | |
dc.identifier.issn | 2372-9198 | |
dc.identifier.uri | https://hdl.handle.net/10642/8405 | |
dc.description.abstract | We report work in progress from interdisciplinary
research on Assisted Living Technology in smart homes for older
adults with mild cognitive impairments or dementia. We present
our field trial, the set-up for collecting and storing data from real
homes, and preliminary results on action recognition using low
resolution depth video cameras. The data have been collected
from seven apartments with one resident each over a period
of two weeks. We propose a pre-processing of the depth videos
by applying an Infinite Response Filter (IIR) for extracting the
movements in the frames prior to classification. In this work
we classify four actions: TV interaction (turn it on/ off and
switch over), standing up, sitting down, and no movement. Our
first results indicate that using the IIR filter for movement
information extraction improves accuracy and can be an efficient
method for recognizing actions. Our current implementation uses
a convolutional long short-term memory (ConvLSTM) neural
network, and achieved an average peak accuracy of 86%. | en |
dc.description.sponsorship | Financed by the Norwegian Research Council under the SAMANSVAR programme (247620/O70). | en |
dc.language.iso | en | en |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en |
dc.relation.ispartofseries | Annual IEEE Symposium on Computer-Based Medical Systems;2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS) | |
dc.rights | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media,
including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en |
dc.subject | Depth videos | en |
dc.subject | Neural networks | en |
dc.subject | Smart homes | en |
dc.subject | Low resolutions | en |
dc.subject | Action recognition | en |
dc.title | Action Recognition in Real Homes using Low Resolution Depth Video Data | en |
dc.type | Conference object | en |
dc.date.updated | 2020-01-10T12:54:35Z | |
dc.description.version | acceptedVersion | en |
dc.identifier.doi | https://dx.doi.org/10.1109/CBMS.2019.00041 | |
dc.identifier.cristin | 1749599 | |
dc.source.journal | IEEE International Symposium on Computer-Based Medical Systems | |