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Out-of-home activity analysis using a low-resolution visual sensor

Mohamed Eldib UGent, Francis Deboeverie, Dirk Van Haerenborgh, Somaya Ben Allouch, Wilfried Philips UGent and Hamid Aghajan UGent (2016) 15. p.79-79
abstract
Loneliness and social isolation are probably the most prevalent psychosocial problems related to aging. One critical component in assessing social isolation in an unobtrusive manner is to measure the out-of-home activity levels, as social isolation often goes along with decreased physical activity, decreased motoric functioning, and a decline in activities of daily living, all of which may lead to a reduction in the amount of time spent out-of-home. In this work, we propose to use a single visual sensor for detecting out-of-home activity. The visual sensor has a very low spatial resolution (900 pixels), which is a key feature to ensure a cheap technology and to maintain the user’s privacy. Firstly, the visual sensor is installed in a top view setup at the door entrance. Secondly, a correlation-based foreground detection method is used to extract the foreground. Thirdly, an Extra Trees Classifier (ETC) is trained to classify the directionality of the person (in/out) based on the motion of the foreground pixels. Due to the nature of variability of the out-of-home activity, the relative frequency of the directionality (in/out) is measured over a window of 3 seconds to determine the final result. We installed our system in 9 different service flats in the UK, Belgium and France where the same ETC model is used. We evaluate our method on video sequences captured in real-life environments from the different setups, where the persons’ out-of-home routines are recorded. The results show that our approach of detecting out-of-home activity achieves an accuracy of 91.30%.
Please use this url to cite or link to this publication:
author
organization
year
type
conference (proceedingsPaper)
publication status
published
subject
keyword
visual sensor, out-of-home, Ambient assisted living (AAL)
volume
15
pages
1 pages
publisher
Gerontechnology
conference name
10th World Conference of Gerontechnology (ISG 2016)
conference location
Nice, France
conference start
2016-09-28
conference end
2016-09-30
ISSN
1569-1101
DOI
10.4017/gt.2016.15.s.825.00
project
SONOPA
language
English
UGent publication?
yes
classification
U
additional info
http://dx.doi.org/10.4017/gt.2016.15.s.825.00
id
7221910
handle
http://hdl.handle.net/1854/LU-7221910
alternative location
https://journal.gerontechnology.org/currentIssueContent.aspx?aid=2370
date created
2016-05-19 16:41:58
date last changed
2018-07-09 19:23:20
@inproceedings{7221910,
  abstract     = {Loneliness and social isolation are probably the most prevalent 
psychosocial problems related to aging. One critical component in 
assessing social isolation in an unobtrusive manner is to measure the 
out-of-home activity levels, as  social isolation often goes along with 
decreased physical activity, decreased motoric functioning, and a decline 
in activities of daily living, all of which may lead to a reduction in the 
amount of time spent out-of-home. In this work, we propose to use a 
single visual sensor for detecting out-of-home activity. The visual 
sensor has a very low spatial resolution (900 pixels), which is a key 
feature to ensure a cheap technology and to maintain the user{\textquoteright}s 
privacy. Firstly, the visual sensor is installed in a top view setup at 
the door entrance. Secondly, a correlation-based foreground detection 
method is used to extract the foreground. Thirdly, an Extra Trees 
Classifier (ETC) is trained to classify the directionality of the person 
(in/out) based on the motion of the foreground pixels. Due to the nature 
of variability of the out-of-home activity, the relative frequency of  
the directionality (in/out) is measured over a window of 3 seconds to 
determine the final result. We installed our system in 9 different 
service flats in the UK, Belgium and France where the same ETC model is 
used. We evaluate our method on video sequences captured in real-life 
environments from the different setups, where the persons{\textquoteright} out-of-home 
routines are recorded. The results show that our approach of detecting 
out-of-home activity achieves an accuracy of 91.30\%.},
  author       = {Eldib, Mohamed and Deboeverie, Francis and Van Haerenborgh, Dirk and Ben Allouch, Somaya and Philips, Wilfried and Aghajan, Hamid},
  issn         = {1569-1101},
  keyword      = {visual sensor,out-of-home,Ambient assisted living (AAL)},
  language     = {eng},
  location     = {Nice, France},
  pages        = {79--79},
  publisher    = {Gerontechnology},
  title        = {Out-of-home activity analysis using a low-resolution visual sensor},
  url          = {http://dx.doi.org/10.4017/gt.2016.15.s.825.00},
  volume       = {15},
  year         = {2016},
}

Chicago
Eldib, Mohamed, Francis Deboeverie, Dirk Van Haerenborgh, Somaya Ben Allouch, Wilfried Philips, and Hamid Aghajan. 2016. “Out-of-home Activity Analysis Using a Low-resolution Visual Sensor.” In , 15:79–79. Gerontechnology.
APA
Eldib, M., Deboeverie, F., Van Haerenborgh, D., Ben Allouch, S., Philips, W., & Aghajan, H. (2016). Out-of-home activity analysis using a low-resolution visual sensor (Vol. 15, pp. 79–79). Presented at the 10th World Conference of Gerontechnology (ISG 2016), Gerontechnology.
Vancouver
1.
Eldib M, Deboeverie F, Van Haerenborgh D, Ben Allouch S, Philips W, Aghajan H. Out-of-home activity analysis using a low-resolution visual sensor. Gerontechnology; 2016. p. 79–79.
MLA
Eldib, Mohamed, Francis Deboeverie, Dirk Van Haerenborgh, et al. “Out-of-home Activity Analysis Using a Low-resolution Visual Sensor.” Vol. 15. Gerontechnology, 2016. 79–79. Print.