Action Recognition in Real Homes using Low Resolution Depth Video Data
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Accepted version
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https://hdl.handle.net/10642/8405Utgivelsesdato
2019-08-05Metadata
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Originalversjon
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 https://dx.doi.org/10.1109/CBMS.2019.00041Sammendrag
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%.