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Journal of Electronic Imaging • Open Access

Behavior analysis for elderly care using a network of low-resolution visual sensors
Author(s): Mohamed Eldib; Francis Deboeverie; Wilfried Philips; Hamid Aghajan

Paper 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.

Paper Details

Date Published: 28 March 2016
PDF: 17 pages
J. Electron. Imaging. 25(4) 041003 doi: 10.1117/1.JEI.25.4.041003
Published in: Journal of Electronic Imaging Volume 25, Issue 4
Show Author Affiliations
Mohamed Eldib, Univ. Gent (Belgium)
Francis Deboeverie, Univ. Gent (Belgium)
Wilfried Philips, Univ. Gent (Belgium)
Hamid Aghajan, Univ. Gent (Belgium)
Ambient Intelligence Research Lab. (United States)


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