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Behavior analysis for elderly care using a network of low-resolution visual sensors

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Abstract
Recent advancements in visual sensor technologies have made behavior analysis practical for in-home monitoring systems. The current in-home monitoring systems face several challenges: (1) visual sensor calibration is a difficult task and not practical in real-life because of the need for recalibration when the visual sensors are moved accidentally by a caregiver or the senior citizen, (2) privacy concerns, and (3) the high hardware installation cost. We propose to use a network of cheap low-resolution visual sensors (30×30  pixels) for long-term behavior analysis. The behavior analysis starts by visual feature selection based on foreground/background detection to track the motion level in each visual sensor. Then a hidden Markov model (HMM) is used to estimate the user’s locations without calibration. Finally, an activity discovery approach is proposed using spatial and temporal contexts. We performed experiments on 10 months of real-life data. We show that the HMM approach outperforms the k-nearest neighbor classifier against ground truth for 30 days. Our framework is able to discover 13 activities of daily livings (ADL parameters). More specifically, we analyze mobility patterns and some of the key ADL parameters to detect increasing or decreasing health conditions.
Keywords
visual sensor networks, ambient assisted living, behavior analysis, hidden Markov model

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MLA
Eldib, Mohamed, et al. “Behavior Analysis for Elderly Care Using a Network of Low-Resolution Visual Sensors.” JOURNAL OF ELECTRONIC IMAGING, vol. 25, no. 4, 2016, pp. 1–17, doi:10.1117/1.JEI.25.4.041003.
APA
Eldib, M., Deboeverie, F., Philips, W., & Aghajan, H. (2016). Behavior analysis for elderly care using a network of low-resolution visual sensors. JOURNAL OF ELECTRONIC IMAGING, 25(4), 1–17. https://doi.org/10.1117/1.JEI.25.4.041003
Chicago author-date
Eldib, Mohamed, Francis Deboeverie, Wilfried Philips, and Hamid Aghajan. 2016. “Behavior Analysis for Elderly Care Using a Network of Low-Resolution Visual Sensors.” JOURNAL OF ELECTRONIC IMAGING 25 (4): 1–17. https://doi.org/10.1117/1.JEI.25.4.041003.
Chicago author-date (all authors)
Eldib, Mohamed, Francis Deboeverie, Wilfried Philips, and Hamid Aghajan. 2016. “Behavior Analysis for Elderly Care Using a Network of Low-Resolution Visual Sensors.” JOURNAL OF ELECTRONIC IMAGING 25 (4): 1–17. doi:10.1117/1.JEI.25.4.041003.
Vancouver
1.
Eldib M, Deboeverie F, Philips W, Aghajan H. Behavior analysis for elderly care using a network of low-resolution visual sensors. JOURNAL OF ELECTRONIC IMAGING. 2016;25(4):1–17.
IEEE
[1]
M. Eldib, F. Deboeverie, W. Philips, and H. Aghajan, “Behavior analysis for elderly care using a network of low-resolution visual sensors,” JOURNAL OF ELECTRONIC IMAGING, vol. 25, no. 4, pp. 1–17, 2016.
@article{7166424,
  abstract     = {{Recent advancements in visual sensor technologies have made behavior analysis practical for in-home monitoring systems. The current in-home monitoring systems face several challenges: (1) visual sensor calibration is a difficult task and not practical in real-life because of the need for recalibration when the visual sensors are moved accidentally by a caregiver or the senior citizen, (2) privacy concerns, and (3) the high hardware installation cost. We propose to use a network of cheap low-resolution visual sensors (30×30  pixels) for long-term behavior analysis. The behavior analysis starts by visual feature selection based on foreground/background detection to track the motion level in each visual sensor. Then a hidden Markov model (HMM) is used to estimate the user’s locations without calibration. Finally, an activity discovery approach is proposed using spatial and temporal contexts. We performed experiments on 10 months of real-life data. We show that the HMM approach outperforms the k-nearest neighbor classifier against ground truth for 30 days. Our framework is able to discover 13 activities of daily livings (ADL parameters). More specifically, we analyze mobility patterns and some of the key ADL parameters to detect increasing or decreasing health conditions.}},
  articleno    = {{041003}},
  author       = {{Eldib, Mohamed and Deboeverie, Francis and Philips, Wilfried and Aghajan, Hamid}},
  issn         = {{1017-9909}},
  journal      = {{JOURNAL OF ELECTRONIC IMAGING}},
  keywords     = {{visual sensor networks,ambient assisted living,behavior analysis,hidden Markov model}},
  language     = {{eng}},
  number       = {{4}},
  pages        = {{041003:1--041003:17}},
  title        = {{Behavior analysis for elderly care using a network of low-resolution visual sensors}},
  url          = {{http://doi.org/10.1117/1.JEI.25.4.041003}},
  volume       = {{25}},
  year         = {{2016}},
}

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